-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/P
> erspectives Final Submission/Data and Replication Files/stratdisc.smcl
  log type:  smcl
 opened on:  22 Jun 2020, 12:34:41

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the General Social Survey portion of F
> igure 1.1//
. 
. //First, download and save the GSS 1972-2016 cumulative file.//
. //It can be obtained here: https://doi.org/10.3886/ICPSR36797.v1 // 
. 
. **BASIC SETUP**
. 
. //Start by loading your saved copy of the GSS dataset.//
. 
. set maxvar 10000


. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/GSS19722016.dta"
(General Social Survey, 1972-2016 [Cumulative File])

. 
. //Obviously you'll need to point STATA to your saved copy of the dataset.//
. 
. //Now, establish the proper settings for weighting and design-corrected stand
> ard errors.//
. 
. svyset [weight=WTSSALL], strat(VSTRAT) psu(VPSU) singleunit(scaled)
(sampling weights assumed)

      pweight: WTSSALL
          VCE: linearized
  Single unit: scaled
     Strata 1: VSTRAT
         SU 1: VPSU
        FPC 1: <zero>

. 
. **CLEAN THE KEY VARIABLES**
. 
. //The "not vote for a woman president" variable is called FEPRES//
. //Let's start by cleaning the FEPRES variable//
. //We'll create a new dummy variable called notvotewoman//
. 
. gen notvotewoman=.
(62,466 missing values generated)

. replace notvotewoman=0 if FEPRES==1
(23,257 real changes made)

. //These are the folks who said they WOULD vote for a woman pres.//
. replace notvotewoman=1 if FEPRES==2
(3,531 real changes made)

. //These are the folks who said they WOULD NOT vote for a woman pres.//
. replace notvotewoman=1 if FEPRES==5
(4 real changes made)

. //These are the folks who said they WOULD NOT vote period.//
. replace notvotewoman=1 if FEPRES==8
(839 real changes made)

. //These are the folks who said they "don't know" if they would vote for a wom
> an pres.//
. replace notvotewoman=1 if FEPRES==9
(74 real changes made)

. //These are the folks who did not answer the question about a woman pres.//
. 
. //As explained in the manuscript, I am coding 0 for everyone who did not expl
> icitly//
. //say they WOULD vote for a woman for president.//
. 
. //Now, let's turn to race.//
. //The "not vote for a black president" variable is called RACPRES//
. 
. //For this project, we should use data from 1974 and 1978 and later ONLY.//
. //In other years, this question was asked of nonblack respondents only, not//
. //all respondents.//
. 
. //We're going to create a new dummy variable called notvoteblack//
. 
. gen notvoteblack=.
(62,466 missing values generated)

. replace notvoteblack=0 if RACPRES==1
(20,072 real changes made)

. //These are the folks who said they WOULD vote for a black pres.//
. replace notvoteblack=1 if RACPRES==2
(3,140 real changes made)

. //These are the folks who said they WOULD NOT vote for a black pres.//
. replace notvoteblack=1 if RACPRES==5
(0 real changes made)

. //These are the folks who said they WOULD NOT vote period.//
. replace notvoteblack=1 if RACPRES==8
(939 real changes made)

. //These are the folks who said they "don't know" if they would vote for a bla
> ck pres.//
. replace notvoteblack=1 if RACPRES==9
(114 real changes made)

. //These are the folks who did not answer the question about a black pres.//
. 
. //As explained in the manuscript, I am coding 0 for everyone who did not expl
> icitly//
. //say they WOULD vote for a black person for president.//
. 
. **ANALYSIS FOR FIGURE 1**
. 
. //The code below produces the estimated population proportions and confidence
>  intervals//
. //shown in Figure 1.1 in the manuscript.//
. 
. //First, we'll look at willingess to vote for a WOMAN president//
. 
. mean notvotewoman[aweight=WTSSALL] if YEAR==1972

Mean estimation                   Number of obs   =      1,613

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .2963881    .011374      .2740787    .3186976
--------------------------------------------------------------

. mean notvotewoman[aweight=WTSSALL] if YEAR==1974

Mean estimation                   Number of obs   =      1,484

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .2260846    .010862       .204778    .2473912
--------------------------------------------------------------

. //Survey design was recorded differently in 1972 and 74, so you need to use t
> his//
. //slightly different code above.//
. svy,subpop(if YEAR==1975): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      50        Number of obs   =      1,490
Number of PSUs   =     100        Population size =  1,489.768
                                  Subpop. no. obs =      1,490
                                  Subpop. size    =  1,489.768
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .2223953   .0118331      .1986279    .2461627
--------------------------------------------------------------
Note: 2151 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1977): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      50        Number of obs   =      1,530
Number of PSUs   =     100        Population size = 1,530.1445
                                  Subpop. no. obs =      1,530
                                  Subpop. size    = 1,530.1445
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .2247518   .0136885      .1972577     .252246
--------------------------------------------------------------
Note: 2151 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1978): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      50        Number of obs   =      1,532
Number of PSUs   =     100        Population size =  1,532.091
                                  Subpop. no. obs =      1,532
                                  Subpop. size    =  1,532.091
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1929965    .011283       .170334     .215659
--------------------------------------------------------------
Note: 2151 strata omitted because they contain no
      subpopulation members.

. mean notvotewoman[aweight=OVERSAMP] if YEAR==1982

Mean estimation                   Number of obs   =      1,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1714239    .008741      .1542807    .1885672
--------------------------------------------------------------

. //The sample was drawn differently in 1982, so that year uses a different wei
> ght.//
. svy,subpop(if YEAR==1983): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      81        Number of obs   =      1,599
Number of PSUs   =     162        Population size = 1,598.8596
                                  Subpop. no. obs =      1,599
                                  Subpop. size    = 1,598.8596
                                  Design df       =         81

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1635554   .0120409      .1395978    .1875131
--------------------------------------------------------------
Note: 2120 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1985): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      54        Number of obs   =      1,534
Number of PSUs   =     108        Population size = 1,534.2054
                                  Subpop. no. obs =      1,534
                                  Subpop. size    = 1,534.2054
                                  Design df       =         54

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1975677    .010038      .1774428    .2176927
--------------------------------------------------------------
Note: 2147 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1986): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      54        Number of obs   =      1,470
Number of PSUs   =     108        Population size = 1,470.0084
                                  Subpop. no. obs =      1,470
                                  Subpop. size    = 1,470.0084
                                  Design df       =         54

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1603289   .0117213      .1368292    .1838286
--------------------------------------------------------------
Note: 2147 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1988): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      66        Number of obs   =        988
Number of PSUs   =     131        Population size =   983.5091
                                  Subpop. no. obs =        988
                                  Subpop. size    =   983.5091
                                  Design df       =         65

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |    .140464   .0148611      .1107843    .1701436
--------------------------------------------------------------
Note: 2135 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1989): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      65        Number of obs   =      1,006
Number of PSUs   =     129        Population size = 1,016.8782
                                  Subpop. no. obs =      1,006
                                  Subpop. size    = 1,016.8782
                                  Design df       =         64

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1813157   .0123865      .1565709    .2060606
--------------------------------------------------------------
Note: 2136 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1990): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      64        Number of obs   =        928
Number of PSUs   =     125        Population size =   943.1281
                                  Subpop. no. obs =        928
                                  Subpop. size    =   943.1281
                                  Design df       =         61

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1245059   .0130643      .0983822    .1506296
--------------------------------------------------------------
Note: 2137 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1991): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      64        Number of obs   =      1,024
Number of PSUs   =     128        Population size = 1,013.2686
                                  Subpop. no. obs =      1,024
                                  Subpop. size    = 1,013.2686
                                  Design df       =         64

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1289322   .0127286      .1035038    .1543605
--------------------------------------------------------------
Note: 2137 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1993): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      86        Number of obs   =      1,080
Number of PSUs   =     168        Population size = 1,080.8633
                                  Subpop. no. obs =      1,080
                                  Subpop. size    = 1,080.8633
                                  Design df       =         82

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .1344741   .0129602      .1086921     .160256
--------------------------------------------------------------
Note: 2115 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1994): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     106        Number of obs   =      1,977
Number of PSUs   =     211        Population size =  1,966.318
                                  Subpop. no. obs =      1,977
                                  Subpop. size    =  1,966.318
                                  Design df       =        105

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .0985407   .0066518      .0853515    .1117299
--------------------------------------------------------------
Note: 2095 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1996): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     104        Number of obs   =      1,960
Number of PSUs   =     208        Population size =  1,973.418
                                  Subpop. no. obs =      1,960
                                  Subpop. size    =  1,973.418
                                  Design df       =        104

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .0963392   .0091872      .0781206    .1145578
--------------------------------------------------------------
Note: 2097 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1998): mean notvotewoman
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     104        Number of obs   =      1,871
Number of PSUs   =     208        Population size = 1,884.9619
                                  Subpop. no. obs =      1,871
                                  Subpop. size    = 1,884.9619
                                  Design df       =        104

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .0957157   .0072852      .0812689    .1101624
--------------------------------------------------------------
Note: 2097 strata omitted because they contain no
      subpopulation members.

. //Sampling was done differently after 2004, so thes years below have differen
> t weights//
. mean notvotewoman[aweight=WTSSNR] if YEAR==2008

Mean estimation                   Number of obs   =      1,329

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .0739258     .00718      .0598405    .0880111
--------------------------------------------------------------

. mean notvotewoman[aweight=WTSSNR] if YEAR==2010

Mean estimation                   Number of obs   =      1,430

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvotewoman |   .0488417   .0057017      .0376571    .0600264
--------------------------------------------------------------

. 
. //Now, we'll look at willingness to vote for a BLACK president//
. 
. mean notvoteblack[aweight=WTSSALL] if YEAR==1974

Mean estimation                   Number of obs   =      1,484

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .2019112    .010424      .1814638    .2223586
--------------------------------------------------------------

. //The structure of the 1974 data requires the code above.//
. svy,subpop(if YEAR==1978): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      50        Number of obs   =      1,532
Number of PSUs   =     100        Population size =  1,532.091
                                  Subpop. no. obs =      1,532
                                  Subpop. size    =  1,532.091
                                  Design df       =         50

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1863869   .0124873      .1613054    .2114684
--------------------------------------------------------------
Note: 2151 strata omitted because they contain no
      subpopulation members.

. mean notvoteblack[aweight=OVERSAMP] if YEAR==1982

Mean estimation                   Number of obs   =      1,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |    .173696   .0087867      .1564632    .1909288
--------------------------------------------------------------

. //We need to use a different weight for 1982, because that year included an o
> versample of Black Americans//
. svy,subpop(if YEAR==1983): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      81        Number of obs   =      1,599
Number of PSUs   =     162        Population size = 1,598.8596
                                  Subpop. no. obs =      1,599
                                  Subpop. size    = 1,598.8596
                                  Design df       =         81

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1844356   .0114303       .161693    .2071783
--------------------------------------------------------------
Note: 2120 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1985): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      54        Number of obs   =      1,534
Number of PSUs   =     108        Population size = 1,534.2054
                                  Subpop. no. obs =      1,534
                                  Subpop. size    = 1,534.2054
                                  Design df       =         54

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1847346   .0119996      .1606768    .2087924
--------------------------------------------------------------
Note: 2147 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1986): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      54        Number of obs   =      1,470
Number of PSUs   =     108        Population size = 1,470.0084
                                  Subpop. no. obs =      1,470
                                  Subpop. size    = 1,470.0084
                                  Design df       =         54

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1473107    .011869      .1235148    .1711067
--------------------------------------------------------------
Note: 2147 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1988): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      66        Number of obs   =        988
Number of PSUs   =     131        Population size =   983.5091
                                  Subpop. no. obs =        988
                                  Subpop. size    =   983.5091
                                  Design df       =         65

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .2139377   .0171805      .1796259    .2482495
--------------------------------------------------------------
Note: 2135 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1989): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      65        Number of obs   =      1,006
Number of PSUs   =     129        Population size = 1,016.8782
                                  Subpop. no. obs =      1,006
                                  Subpop. size    = 1,016.8782
                                  Design df       =         64

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .2119534   .0173067      .1773793    .2465275
--------------------------------------------------------------
Note: 2136 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1990): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      64        Number of obs   =        928
Number of PSUs   =     125        Population size =   943.1281
                                  Subpop. no. obs =        928
                                  Subpop. size    =   943.1281
                                  Design df       =         61

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1616898   .0161156      .1294647     .193915
--------------------------------------------------------------
Note: 2137 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1991): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      64        Number of obs   =      1,024
Number of PSUs   =     128        Population size = 1,013.2686
                                  Subpop. no. obs =      1,024
                                  Subpop. size    = 1,013.2686
                                  Design df       =         64

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1263112   .0130393      .1002622    .1523602
--------------------------------------------------------------
Note: 2137 strata omitted because they contain no
      subpopulation members.

. svy,subpop(if YEAR==1993): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      86        Number of obs   =      1,080
Number of PSUs   =     168        Population size = 1,080.8633
                                  Subpop. no. obs =      1,080
                                  Subpop. size    = 1,080.8633
                                  Design df       =         82

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1525667   .0148492      .1230269    .1821064
--------------------------------------------------------------
Note: 2115 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1994): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     106        Number of obs   =      1,977
Number of PSUs   =     211        Population size =  1,966.318
                                  Subpop. no. obs =      1,977
                                  Subpop. size    =  1,966.318
                                  Design df       =        105

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .1222129   .0092263      .1039188    .1405071
--------------------------------------------------------------
Note: 2095 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. svy,subpop(if YEAR==1996): mean notvoteblack
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     103        Number of obs   =        991
Number of PSUs   =     202        Population size =    995.128
                                  Subpop. no. obs =        991
                                  Subpop. size    =    995.128
                                  Design df       =         99

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |    .099891   .0126143      .0748614    .1249205
--------------------------------------------------------------
Note: 2097 strata omitted because they contain no
      subpopulation members.
Note: Variance scaled to handle strata with a single sampling
      unit.

. //Sampling was done differently after 2004, so these years have different wei
> ghts//
. mean notvoteblack[aweight=WTSSNR] if YEAR==2008

Mean estimation                   Number of obs   =      1,329

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .0776095    .007342      .0632062    .0920127
--------------------------------------------------------------

. mean notvoteblack[aweight=WTSSNR] if YEAR==2010

Mean estimation                   Number of obs   =      1,430

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
notvoteblack |   .0477961   .0056435      .0367258    .0588665
--------------------------------------------------------------

. 
. clear

. 
. **That's the end of the GSS data analysis for Figure 1.1.**
. **Next, please proceed to Study1_Figure1.do**
. 
end of do-file

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the Study 1 portion of Figure 1.1//
. 
. //First, download and save the file, Study1.dta //
. //It is part of this replication package // 
. 
. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/Study1.dta"

. 
. //Of course your version of the dataset is saved differently. Go open it.//
. 
. //Then, go find the subjects' mean estimates//
. 
. sum notvotewoman

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,912    46.88075    26.27055          0        100

. sum notvoteblack

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,912    42.26412     26.9656          0        100

. 
. //That's all! These results are depicted in the vertical lines in Figure 1.1/
> /
. //To reproduce the main & supplemental results for Studies 1, 2, and 3, pleas
> e continue//
. //by running the other do-files in this replication package.//
. 
. clear

. 
end of do-file

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the Study 1 results//
. 
. //First, the do-file cleans and re-organizes the dataset.//
. //Then, the "Analysis 1" section provides the main results in the manuscript.
> //
. //Last, the "Analysis 2" section provides supplemental analysis cited in the 
> manuscript and the appendix.//
. 
. **GET THE DATASET**
. 
. //Download and save the file Study1.dta //
. //It is part of this replication package // 
. 
. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/Study1.dta"

. 
. //Of course your version of the dataset is saved differently. Go open it.//
. 
. **CLEAN THE DATA AND SET UP VARIABLES**
. 
. gen female=0

. replace female=1 if gender=="Female"
(993 real changes made)

. rename female femaleresp

. 
. gen male=0

. replace male=1 if gender=="Male"
(913 real changes made)

. rename male maleresp

. 
. encode birthyear, gen(birthyear2)

. drop birthyear

. rename birthyear2 birthyear

. 
. gen millenial=0

. replace millenial=1 if birthyear>56
(600 real changes made)

. **For some reason 1984 is coded as 56** 
. 
. gen boomerandabove=0

. replace boomerandabove=1 if birthyear<36
(634 real changes made)

. **1965 is coded as 36**
. 
. gen white=0

. replace white=1 if race=="Caucasian/White (non-Hispanic)"
(1,327 real changes made)

. rename white whiteresp

. 
. gen black=0

. replace black=1 if race=="Black or African-American (non-Hispanic)"
(227 real changes made)

. rename black blackresp

. 
. gen hispanic=0

. replace hispanic=1 if race=="Latino or Hispanic"
(131 real changes made)

. rename hispanic hispanicresp

. 
. gen api=0

. replace api=1 if race=="Asian/Pacific Islander"
(92 real changes made)

. rename api apiresp

. **I renamed all the variables above to clarify that they are coding the race 
> of the subject (not the candidate profiles)**
. 
. rename income income2

. gen income=.
(1,948 missing values generated)

. replace income=1 if income2=="Less than $10,000"
(175 real changes made)

. replace income=2 if income2=="$10,000-$19,999"
(246 real changes made)

. replace income=3 if income2=="$20,000-$29,999"
(284 real changes made)

. replace income=4 if income2=="$30,000-$39,999"
(248 real changes made)

. replace income=5 if income2=="$40,000-$49,999"
(181 real changes made)

. replace income=6 if income2=="$50,000-$74,999"
(317 real changes made)

. replace income=7 if income2=="$75,000-$99,999"
(170 real changes made)

. replace income=8 if income2=="$100,000-$149,999"
(150 real changes made)

. replace income=9 if income2=="$150,000 or more"
(96 real changes made)

. drop income2

. 
. rename education education2

. gen education=.
(1,948 missing values generated)

. replace education=1 if education2=="Did not graduate from high school"
(80 real changes made)

. replace education=2 if education2=="High school graduate"
(504 real changes made)

. replace education=3 if education2=="Some college, but no degree"
(520 real changes made)

. replace education=4 if education2=="2-year college degree"
(225 real changes made)

. replace education=5 if education2=="4-year college degree"
(402 real changes made)

. replace education=6 if education2=="Postgraduate degree (MA, MBA, JD, PhD, et
> c.)"
(181 real changes made)

. 
. gen married=0

. replace married=1 if maritalstatus=="Married"
(754 real changes made)

. 
. gen age=90-birthyear
(37 missing values generated)

. 
. rename evangelical evangelical2

. gen evangelical=0

. replace evangelical=1 if evangelical2=="Yes"
(246 real changes made)

. drop evangelical2

. 
. gen conservative=0

. replace conservative=1 if ideology=="Conservative"
(608 real changes made)

. 
. gen liberal=0 

. replace liberal=1 if ideology=="Liberal"
(531 real changes made)

. 
. gen noideology=0

. replace noideology=1 if ideology=="Haven't thought much about it"
(261 real changes made)

. **Note, I think this is another measure of people who are politically disenga
> ged or confused.**
. 
. gen democrat=0

. replace democrat=1 if partyid=="Democrat"
(705 real changes made)

. 
. gen independent=0

. replace independent=1 if partyid=="Independent"
(504 real changes made)

. 
. gen republican=0

. replace republican=1 if partyid=="Republican"
(601 real changes made)

. 
. gen noreligion=0

. replace noreligion=1 if religion=="None"
(383 real changes made)

. 
. gen catholic=0

. replace catholic=1 if religion=="Roman Catholic"
(416 real changes made)

. 
. gen protestant=0

. replace protestant=1 if religion=="Protestant"
(431 real changes made)

. 
. gen west=0

. replace west=1 if state=="Washington"
(40 real changes made)

. replace west=1 if state=="Alaska"
(0 real changes made)

. replace west=1 if state=="Hawaii"
(0 real changes made)

. replace west=1 if state=="California"
(210 real changes made)

. replace west=1 if state=="Arizona"
(41 real changes made)

. replace west=1 if state=="Oregon"
(25 real changes made)

. replace west=1 if state=="Nevada"
(28 real changes made)

. replace west=1 if state=="New Mexico"
(14 real changes made)

. gen mountain=0

. replace mountain=1 if state=="Idaho"
(9 real changes made)

. replace mountain=1 if state=="Montana"
(7 real changes made)

. replace mountain=1 if state=="Colorado"
(22 real changes made)

. replace mountain=1 if state=="Utah"
(13 real changes made)

. replace mountain=1 if state=="Wyoming"
(3 real changes made)

. gen south=0

. replace south=1 if state=="Texas"
(140 real changes made)

. replace south=1 if state=="Alabama"
(40 real changes made)

. replace south=1 if state=="Louisiana"
(28 real changes made)

. replace south=1 if state=="Arkansas"
(18 real changes made)

. replace south=1 if state=="Mississippi"
(19 real changes made)

. replace south=1 if state=="Tennessee"
(44 real changes made)

. replace south=1 if state=="Kentucky"
(21 real changes made)

. replace south=1 if state=="West Virginia"
(16 real changes made)

. replace south=1 if state=="Virginia"
(52 real changes made)

. replace south=1 if state=="South Carolina"
(29 real changes made)

. replace south=1 if state=="North Carolina"
(65 real changes made)

. replace south=1 if state=="Florida"
(148 real changes made)

. replace south=1 if state=="Georgia"
(60 real changes made)

. replace south=1 if state=="Maryland"
(28 real changes made)

. gen east=0

. replace east=1 if state=="Delaware"
(9 real changes made)

. replace east=1 if state=="Pennsylvania"
(98 real changes made)

. replace east=1 if state=="Rhode Island"
(7 real changes made)

. replace east=1 if state=="New Jersey"
(64 real changes made)

. replace east=1 if state=="New York"
(140 real changes made)

. replace east=1 if state=="Connecticut"
(20 real changes made)

. replace east=1 if state=="Massachusetts"
(35 real changes made)

. replace east=1 if state=="Maine"
(9 real changes made)

. replace east=1 if state=="New Hampshire"
(6 real changes made)

. replace east=1 if state=="Vermont"
(2 real changes made)

. gen dc=0

. replace dc=1 if state=="District of Columbia"
(4 real changes made)

. gen midwest=0

. replace midwest=1 if state=="Ohio"
(87 real changes made)

. replace midwest=1 if state=="Michigan"
(48 real changes made)

. replace midwest=1 if state=="Indiana"
(35 real changes made)

. replace midwest=1 if state=="Illinois"
(59 real changes made)

. replace midwest=1 if state=="Iowa"
(11 real changes made)

. replace midwest=1 if state=="Minnesota"
(30 real changes made)

. replace midwest=1 if state=="Wisconsin"
(24 real changes made)

. replace midwest=1 if state=="North Dakota"
(2 real changes made)

. replace midwest=1 if state=="South Dakota"
(2 real changes made)

. replace midwest=1 if state=="Nebraska"
(9 real changes made)

. replace midwest=1 if state=="Kansas"
(9 real changes made)

. replace midwest=1 if state=="Oklahoma"
(19 real changes made)

. replace midwest=1 if state=="Missouri"
(49 real changes made)

. 
. 
. **Create a variable to indicate if the respondent lives in a state that has h
> ad a female governor since 1990 (that is, in the last 30 years).**
. 
. gen femalegov30=0

. replace femalegov30=1 if state=="Kansas"
(9 real changes made)

. replace femalegov30=1 if state=="Oregon"
(25 real changes made)

. replace femalegov30=1 if state=="Texas"
(140 real changes made)

. replace femalegov30=1 if state=="New Jersey"
(64 real changes made)

. replace femalegov30=1 if state=="New Hampshire"
(6 real changes made)

. replace femalegov30=1 if state=="Arizona"
(41 real changes made)

. replace femalegov30=1 if state=="Ohio"
(87 real changes made)

. replace femalegov30=1 if state=="Montana"
(7 real changes made)

. replace femalegov30=1 if state=="Delaware"
(9 real changes made)

. replace femalegov30=1 if state=="Massachusetts"
(35 real changes made)

. replace femalegov30=1 if state=="South Dakota"
(2 real changes made)

. replace femalegov30=1 if state=="Hawaii"
(0 real changes made)

. replace femalegov30=1 if state=="Michigan"
(48 real changes made)

. replace femalegov30=1 if state=="Utah"
(13 real changes made)

. replace femalegov30=1 if state=="Louisiana"
(28 real changes made)

. replace femalegov30=1 if state=="Connecticut"
(20 real changes made)

. replace femalegov30=1 if state=="Washington"
(40 real changes made)

. replace femalegov30=1 if state=="Alaska"
(0 real changes made)

. replace femalegov30=1 if state=="North Carolina"
(65 real changes made)

. replace femalegov30=1 if state=="New Mexico"
(14 real changes made)

. replace femalegov30=1 if state=="Oklahoma"
(19 real changes made)

. replace femalegov30=1 if state=="Maine"
(9 real changes made)

. replace femalegov30=1 if state=="South Carolina"
(29 real changes made)

. replace femalegov30=1 if state=="Rhode Island"
(7 real changes made)

. replace femalegov30=1 if state=="Alabama"
(40 real changes made)

. replace femalegov30=1 if state=="Vermont"
(2 real changes made)

. replace femalegov30=1 if state=="Nebraska"
(9 real changes made)

. replace femalegov30=1 if state=="Iowa"
(11 real changes made)

. 
. **Now create a variable to indicate if the respondent lives in a state that h
> ad a female governor at the time of the experiment.**
. 
. gen femalegovnow=0

. replace femalegovnow=1 if state=="Rhode Island"
(7 real changes made)

. replace femalegovnow=1 if state=="Oregon"
(25 real changes made)

. replace femalegovnow=1 if state=="Alabama"
(40 real changes made)

. replace femalegovnow=1 if state=="Iowa"
(11 real changes made)

. replace femalegovnow=1 if state=="Michigan"
(48 real changes made)

. replace femalegovnow=1 if state=="New Mexico"
(14 real changes made)

. replace femalegovnow=1 if state=="Maine"
(9 real changes made)

. replace femalegovnow=1 if state=="South Dakota"
(2 real changes made)

. replace femalegovnow=1 if state=="Kansas"
(9 real changes made)

. 
. **And create a variable measuring if the respodent lives in a state that had 
> a black gov in the past 30 years (since 1990).**
. gen blackgov30=0

. replace blackgov30=1 if state=="Virginia"
(52 real changes made)

. replace blackgov30=1 if state=="New York"
(140 real changes made)

. replace blackgov30=1 if state=="Massachusetts"
(35 real changes made)

. **Note that there has never been a black female governor of a US state. So al
> l these former govs. were black men.**
. **At the time of my experiments, there were no black governors.**
. 
. **Create dummy variable measuring if the subject lives in a state with a GOP 
> governor in 2019**
. gen gopgov=1

. replace gopgov=0 if state=="California" 
(210 real changes made)

. replace gopgov=. if state=="District of Columbia"
(4 real changes made, 4 to missing)

. replace gopgov=0 if state=="Colorado"
(22 real changes made)

. replace gopgov=0 if state=="Connecticut"
(20 real changes made)

. replace gopgov=0 if state=="Delaware"
(9 real changes made)

. replace gopgov=0 if state=="Hawaii"
(0 real changes made)

. replace gopgov=0 if state=="Illinois"
(59 real changes made)

. replace gopgov=0 if state=="Kansas"
(9 real changes made)

. replace gopgov=0 if state=="Kentucky"
(21 real changes made)

. replace gopgov=0 if state=="Louisiana"
(28 real changes made)

. replace gopgov=0 if state=="Maine"
(9 real changes made)

. replace gopgov=0 if state=="Michigan"
(48 real changes made)

. replace gopgov=0 if state=="Minnesota"
(30 real changes made)

. replace gopgov=0 if state=="Montana"
(7 real changes made)

. replace gopgov=0 if state=="Nevada"
(28 real changes made)

. replace gopgov=0 if state=="New Jersey"
(64 real changes made)

. replace gopgov=0 if state=="New Mexico"
(14 real changes made)

. replace gopgov=0 if state=="New York"
(140 real changes made)

. replace gopgov=0 if state=="North Carolina"
(65 real changes made)

. replace gopgov=0 if state=="Oregon"
(25 real changes made)

. replace gopgov=0 if state=="Pennsylvania"
(98 real changes made)

. replace gopgov=0 if state=="Rhode Island"
(7 real changes made)

. replace gopgov=0 if state=="Virginia"
(52 real changes made)

. replace gopgov=0 if state=="Washington"
(40 real changes made)

. replace gopgov=0 if state=="Wisconsin"
(24 real changes made)

. 
. **Create variables measuring political knowledge.**
. **"Q147: Whose responsibility is it to decide if a law is constitutional or n
> ot?"**
. 
. gen constitutional2=0

. **0 reflects wrong answers or no answer.**
. replace constitutional2=1 if constitutional=="The Supreme Court"
(1,233 real changes made)

. 
. **"Q149: Whose responsibility is it to nominate judges to Federal Courts?"**
. 
. gen nominatejudges2=0

. replace nominatejudges2=1 if nominatejudges=="The President"
(1,003 real changes made)

. 
. **"Q153: Do you know what job or political office is currently held by Nancy 
> Pelosi?"**
. 
. gen pelosi2=0

. replace pelosi2=1 if pelosi=="Speaker of the House"
(1,167 real changes made)

. 
. **"Q155: Do you know what job or political office is currently held by Steve 
> Mnuchin?"**
. 
. gen mnuchin2=0

. replace mnuchin2=1 if mnuchin=="Treasury Secretary"
(568 real changes made)

. 
. gen polknowledge=mnuchin2+pelosi2+constitutional2+nominatejudges2

. **This creates a single political knowledge variable measuring how many of th
> ese questions the respondents got right.**
. 
. **Now we are going to create a measure of attentiveness.**
. 
. gen highquality=0

. replace highquality=1 if redgreen=="Red,Green"
(1,254 real changes made)

. **In an initial survey question, subjects were told to choose red and green a
> s their favorite colors,**
. **no matter what their favorite colors really are. Here, subjects who answere
> d correctly are coded**
. **as high-quality subjects.**
. 
. **generate IDs**
. gen id=_n

. order id

. 
. **fix coding of electability scores**
. gen ceomw2=.
(1,948 missing values generated)

. replace ceomw2=1 if ceomw=="Very unelectable"
(58 real changes made)

. replace ceomw2=2 if ceomw=="Somewhat unelectable"
(99 real changes made)

. replace ceomw2=3 if ceomw=="Somewhat electable"
(222 real changes made)

. replace ceomw2=4 if ceomw=="Very electable"
(106 real changes made)

. drop ceomw

. rename ceomw2 ceomw

. //ceomw refers to the white, male CEO candidate profile//
. 
. gen ceomb2=.
(1,948 missing values generated)

. replace ceomb2=1 if ceomb=="Very unelectable"
(55 real changes made)

. replace ceomb2=2 if ceomb=="Somewhat unelectable"
(115 real changes made)

. replace ceomb2=3 if ceomb=="Somewhat electable"
(208 real changes made)

. replace ceomb2=4 if ceomb=="Very electable"
(97 real changes made)

. drop ceomb

. rename ceomb2 ceomb

. //ceomb refers to the black, male CEO candidate profile//
. 
. gen ceofw2=.
(1,948 missing values generated)

. replace ceofw2=1 if ceofw=="Very unelectable"
(48 real changes made)

. replace ceofw2=2 if ceofw=="Somewhat unelectable"
(132 real changes made)

. replace ceofw2=3 if ceofw=="Somewhat electable"
(222 real changes made)

. replace ceofw2=4 if ceofw=="Very electable"
(84 real changes made)

. drop ceofw

. rename ceofw2 ceofw

. //ceofw refers to the female, white CEO candidate profile//
. 
. gen ceofb2=.
(1,948 missing values generated)

. replace ceofb2=1 if ceofb=="Very unelectable"
(50 real changes made)

. replace ceofb2=2 if ceofb=="Somewhat unelectable"
(116 real changes made)

. replace ceofb2=3 if ceofb=="Somewhat electable"
(211 real changes made)

. replace ceofb2=4 if ceofb=="Very electable"
(89 real changes made)

. drop ceofb

. rename ceofb2 ceofb

. //ceomb refers to the black, female CEO candidate profile//
. 
. gen agmw2=.
(1,948 missing values generated)

. replace agmw2=1 if agmw=="Very unelectable"
(10 real changes made)

. replace agmw2=2 if agmw=="Somewhat unelectable"
(30 real changes made)

. replace agmw2=3 if agmw=="Somewhat electable"
(198 real changes made)

. replace agmw2=4 if agmw=="Very electable"
(243 real changes made)

. drop agmw

. rename agmw2 agmw

. //agmw refers to the male white attorney general profile//
. 
. gen agfw2=.
(1,948 missing values generated)

. replace agfw2=1 if agfw=="Very unelectable"
(10 real changes made)

. replace agfw2=2 if agfw=="Somewhat unelectable"
(40 real changes made)

. replace agfw2=3 if agfw=="Somewhat electable"
(221 real changes made)

. replace agfw2=4 if agfw=="Very electable"
(216 real changes made)

. drop agfw

. rename agfw2 agfw

. //agfw refers to the female white attorney general profile//
. 
. gen agmb2=.
(1,948 missing values generated)

. replace agmb2=1 if agmb=="Very unelectable"
(19 real changes made)

. replace agmb2=2 if agmb=="Somewhat unelectable"
(34 real changes made)

. replace agmb2=3 if agmb=="Somewhat electable"
(198 real changes made)

. replace agmb2=4 if agmb=="Very electable"
(223 real changes made)

. drop agmb

. rename agmb2 agmb

. //agmb refers to the male black attorney general profile//
. 
. gen agfb2=.
(1,948 missing values generated)

. replace agfb2=1 if agfb=="Very unelectable"
(28 real changes made)

. replace agfb2=2 if agfb=="Somewhat unelectable"
(53 real changes made)

. replace agfb2=3 if agfb=="Somewhat electable"
(210 real changes made)

. replace agfb2=4 if agfb=="Very electable"
(179 real changes made)

. drop agfb

. rename agfb2 agfb

. //agfb refers to the black female attorney general profile//
. 
. gen lgmw2=.
(1,948 missing values generated)

. replace lgmw2=1 if lgmw=="Very unelectable"
(15 real changes made)

. replace lgmw2=2 if lgmw=="Somewhat unelectable"
(51 real changes made)

. replace lgmw2=3 if lgmw=="Somewhat electable"
(229 real changes made)

. replace lgmw2=4 if lgmw=="Very electable"
(186 real changes made)

. drop lgmw

. rename lgmw2 lgmw

. //lgmw refers to the male white lt. gov. profile//
. 
. gen lgfw2=.
(1,948 missing values generated)

. replace lgfw2=1 if lgfw=="Very unelectable"
(13 real changes made)

. replace lgfw2=2 if lgfw=="Somewhat unelectable"
(54 real changes made)

. replace lgfw2=3 if lgfw=="Somewhat electable"
(228 real changes made)

. replace lgfw2=4 if lgfw=="Very electable"
(167 real changes made)

. drop lgfw

. rename lgfw2 lgfw

. //lgfw refers to the female white lt. gov. profile//
. 
. gen lgmb2=.
(1,948 missing values generated)

. replace lgmb2=1 if lgmb=="Very unelectable"
(12 real changes made)

. replace lgmb2=2 if lgmb=="Somewhat unelectable"
(48 real changes made)

. replace lgmb2=3 if lgmb=="Somewhat electable"
(240 real changes made)

. replace lgmb2=4 if lgmb=="Very electable"
(180 real changes made)

. drop lgmb

. rename lgmb2 lgmb

. //lgmb refers to the male black lt. gov. profile//
. 
. gen lgfb2=.
(1,948 missing values generated)

. replace lgfb2=1 if lgfb=="Very unelectable"
(24 real changes made)

. replace lgfb2=2 if lgfb=="Somewhat unelectable"
(73 real changes made)

. replace lgfb2=3 if lgfb=="Somewhat electable"
(227 real changes made)

. replace lgfb2=4 if lgfb=="Very electable"
(165 real changes made)

. drop lgfb

. rename lgfb2 lgfb

. //lgfb refers to the female black lt. gov. profile//
. 
. **Generate indicator of male profile**
. 
. **Male2 indicates whether the LG profile was male**
. gen male2=0

. replace male2=1 if lgmb==1
(12 real changes made)

. replace male2=1 if lgmb==2
(48 real changes made)

. replace male2=1 if lgmb==3
(240 real changes made)

. replace male2=1 if lgmb==4
(180 real changes made)

. replace male2=1 if lgmw==1
(15 real changes made)

. replace male2=1 if lgmw==2
(51 real changes made)

. replace male2=1 if lgmw==4
(186 real changes made)

. replace male2=1 if lgmw==3
(229 real changes made)

. 
. **Male1 indicates whether the AG profile was male**
. gen male1=0

. replace male1=1 if agmb==1
(19 real changes made)

. replace male1=1 if agmb==2
(34 real changes made)

. replace male1=1 if agmb==4
(223 real changes made)

. replace male1=1 if agmb==3
(198 real changes made)

. replace male1=1 if agmw==1
(10 real changes made)

. replace male1=1 if agmw==2
(30 real changes made)

. replace male1=1 if agmw==4
(243 real changes made)

. replace male1=1 if agmw==3
(198 real changes made)

. 
. **Male3 indicates whether the CEO Profile was male**
. gen male3=0

. replace male3=1 if ceomb==1
(55 real changes made)

. replace male3=1 if ceomb==2
(115 real changes made)

. replace male3=1 if ceomb==4
(97 real changes made)

. replace male3=1 if ceomb==3
(208 real changes made)

. replace male3=1 if ceomw==1
(58 real changes made)

. replace male3=1 if ceomw==2
(99 real changes made)

. replace male3=1 if ceomw==4
(106 real changes made)

. replace male3=1 if ceomw==3
(222 real changes made)

. 
. **check that this worked**
. hist male1
(bin=32, start=0, width=.03125)

. sum male1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       male1 |      1,948    .4902464    .5000332          0          1

. 
. hist male2
(bin=32, start=0, width=.03125)

. sum male2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       male2 |      1,948    .4933265    .5000838          0          1

. 
. hist male3
(bin=32, start=0, width=.03125)

. sum male3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       male3 |      1,948    .4928131    .5000767          0          1

. 
. **Generate Race Variables**
. gen white1=0

. replace white1=1 if agmw==1
(10 real changes made)

. replace white1=1 if agmw==2
(30 real changes made)

. replace white1=1 if agmw==4
(243 real changes made)

. replace white1=1 if agmw==3
(198 real changes made)

. replace white1=1 if agfw==1
(10 real changes made)

. replace white1=1 if agfw==2
(40 real changes made)

. replace white1=1 if agfw==4
(216 real changes made)

. replace white1=1 if agfw==3
(221 real changes made)

. 
. gen white2=0

. replace white2=1 if lgmw==1
(15 real changes made)

. replace white2=1 if lgmw==2
(51 real changes made)

. replace white2=1 if lgmw==4
(186 real changes made)

. replace white2=1 if lgmw==3
(229 real changes made)

. replace white2=1 if lgfw==1
(13 real changes made)

. replace white2=1 if lgfw==2
(54 real changes made)

. replace white2=1 if lgfw==4
(167 real changes made)

. replace white2=1 if lgfw==3
(228 real changes made)

. 
. gen white3=0

. replace white3=1 if ceomw==1
(58 real changes made)

. replace white3=1 if ceomw==2
(99 real changes made)

. replace white3=1 if ceomw==4
(106 real changes made)

. replace white3=1 if ceomw==3
(222 real changes made)

. replace white3=1 if ceofw==1
(48 real changes made)

. replace white3=1 if ceofw==2
(132 real changes made)

. replace white3=1 if ceofw==4
(84 real changes made)

. replace white3=1 if ceofw==3
(222 real changes made)

. 
. **check that this worked**
. sum white1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      white1 |      1,948    .4969199    .5001189          0          1

. hist white1
(bin=32, start=0, width=.03125)

. 
. sum white2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      white2 |      1,948    .4840862     .499875          0          1

. hist white2
(bin=32, start=0, width=.03125)

. 
. hist white3
(bin=32, start=0, width=.03125)

. sum white3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      white3 |      1,948      .49846     .500126          0          1

. 
. **Create Resume variables**
. gen resume1=.
(1,948 missing values generated)

. replace resume1=1 if agfw==1
(10 real changes made)

. replace resume1=2 if agfw==2
(40 real changes made)

. replace resume1=3 if agfw==3
(221 real changes made)

. replace resume1=4 if agfw==4
(216 real changes made)

. 
. replace resume1=1 if agmw==1
(10 real changes made)

. replace resume1=2 if agmw==2
(30 real changes made)

. replace resume1=3 if agmw==3
(198 real changes made)

. replace resume1=4 if agmw==4
(243 real changes made)

. 
. replace resume1=1 if agfb==1
(28 real changes made)

. replace resume1=2 if agfb==2
(53 real changes made)

. replace resume1=3 if agfb==3
(210 real changes made)

. replace resume1=4 if agfb==4
(179 real changes made)

. 
. replace resume1=1 if agmb==1
(19 real changes made)

. replace resume1=2 if agmb==2
(34 real changes made)

. replace resume1=3 if agmb==3
(198 real changes made)

. replace resume1=4 if agmb==4
(223 real changes made)

. 
. gen resume2=.
(1,948 missing values generated)

. replace resume2=1 if lgfw==1
(13 real changes made)

. replace resume2=2 if lgfw==2
(54 real changes made)

. replace resume2=3 if lgfw==3
(228 real changes made)

. replace resume2=4 if lgfw==4
(167 real changes made)

. 
. replace resume2=1 if lgfb==1
(24 real changes made)

. replace resume2=2 if lgfb==2
(73 real changes made)

. replace resume2=3 if lgfb==3
(227 real changes made)

. replace resume2=4 if lgfb==4
(165 real changes made)

. 
. replace resume2=1 if lgmb==1
(12 real changes made)

. replace resume2=2 if lgmb==2
(48 real changes made)

. replace resume2=3 if lgmb==3
(240 real changes made)

. replace resume2=4 if lgmb==4
(180 real changes made)

. 
. replace resume2=1 if lgmw==1
(15 real changes made)

. replace resume2=2 if lgmw==2
(51 real changes made)

. replace resume2=3 if lgmw==3
(229 real changes made)

. replace resume2=4 if lgmw==4
(186 real changes made)

. 
. gen resume3=.
(1,948 missing values generated)

. replace resume3=1 if ceofw==1
(48 real changes made)

. replace resume3=2 if ceofw==2
(132 real changes made)

. replace resume3=3 if ceofw==3
(222 real changes made)

. replace resume3=4 if ceofw==4
(84 real changes made)

. 
. replace resume3=1 if ceofb==1
(50 real changes made)

. replace resume3=2 if ceofb==2
(116 real changes made)

. replace resume3=3 if ceofb==3
(211 real changes made)

. replace resume3=4 if ceofb==4
(89 real changes made)

. 
. replace resume3=1 if ceomw==1
(58 real changes made)

. replace resume3=2 if ceomw==2
(99 real changes made)

. replace resume3=3 if ceomw==3
(222 real changes made)

. replace resume3=4 if ceomw==4
(106 real changes made)

. 
. replace resume3=1 if ceomb==1
(55 real changes made)

. replace resume3=2 if ceomb==2
(115 real changes made)

. replace resume3=3 if ceomb==3
(208 real changes made)

. replace resume3=4 if ceomb==4
(97 real changes made)

. 
. **check to make sure this worked**
. sum resume1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     resume1 |      1,912    3.298117    .7642158          1          4

. sum resume2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     resume2 |      1,912    3.179916    .7649124          1          4

. sum resume3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     resume3 |      1,912     2.73431    .8997439          1          4

. 
. hist resume1
(bin=32, start=1, width=.09375)

. hist resume2
(bin=32, start=1, width=.09375)

. hist resume3
(bin=32, start=1, width=.09375)

. 
. **RESHAPE**
. reshape long resume male white , i(id) j(profile)
(note: j = 1 2 3)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                     1948   ->    5844
Number of variables                  76   ->      71
j variable (3 values)                     ->   profile
xij variables:
                resume1 resume2 resume3   ->   resume
                      male1 male2 male3   ->   male
                   white1 white2 white3   ->   white
-----------------------------------------------------------------------------

. rename resume electability

. //Now the unit of analysis is the CANDIDATE PROFILE.//
. //EACH SUBJECT RATED 3 CANDIDATE PROFILES//
. 
. **Make candidate profile dummies**
. 
. gen profile1=0

. replace profile1=1 if profile==1
(1,948 real changes made)

. gen profile2=0

. replace profile2=1 if profile==2
(1,948 real changes made)

. gen profile3=0

. replace profile3=1 if profile==3
(1,948 real changes made)

. 
. gen whitewoman=0

. replace whitewoman=1 if white==1 & male==0
(1,435 real changes made)

. gen blackwoman=0

. replace blackwoman=1 if white==0 & male==0
(1,533 real changes made)

. gen blackman=0

. replace blackman=1 if white==0 & male==1
(1,429 real changes made)

. gen whiteman=0

. replace whiteman=1 if white==1 & male==1
(1,447 real changes made)

. 
. **create comparison variables for use in t-tests**
. 
. gen blackmancompare=.
(5,844 missing values generated)

. replace blackmancompare=1 if blackman==1
(1,429 real changes made)

. replace blackmancompare=0 if whiteman==1
(1,447 real changes made)

. **this variable (blackmancompare) should be used in the by() field of a t-tes
> t**
. **it will compare the mean for black male profiles vs. the mean for white mal
> e profiles**
. 
. **Now let's do the same for all the other relevant comparison groups.**
. gen blackwomancompare=.
(5,844 missing values generated)

. replace blackwomancompare=1 if blackwoman==1
(1,533 real changes made)

. replace blackwomancompare=0 if whiteman==1
(1,447 real changes made)

. 
. gen whitewomancompare=.
(5,844 missing values generated)

. replace whitewomancompare=1 if whitewoman==1
(1,435 real changes made)

. replace whitewomancompare=0 if whiteman==1
(1,447 real changes made)

. 
. **Create dummy variables measuring which level of electability was assigned t
> o each profile**
. gen veryelect=.
(5,844 missing values generated)

. replace veryelect=0 if elect==1
(342 real changes made)

. replace veryelect=0 if elect==2
(845 real changes made)

. replace veryelect=0 if elect==3
(2,614 real changes made)

. replace veryelect=1 if elect==4
(1,935 real changes made)

. 
. gen someelect=.
(5,844 missing values generated)

. replace somee=0 if elect==1
(342 real changes made)

. replace somee=0 if elect==2
(845 real changes made)

. replace somee=1 if elect==3
(2,614 real changes made)

. replace somee=0 if elect==4
(1,935 real changes made)

. 
. gen someunelect=.
(5,844 missing values generated)

. replace someu=0 if elect==1
(342 real changes made)

. replace someu=1 if elect==2
(845 real changes made)

. replace someu=0 if elect==3
(2,614 real changes made)

. replace someu=0 if elect==4
(1,935 real changes made)

. 
. gen veryunelect=.
(5,844 missing values generated)

. replace veryu=1 if elect==1
(342 real changes made)

. replace veryu=0 if elect==2
(845 real changes made)

. replace veryu=0 if elect==3
(2,614 real changes made)

. replace veryu=0 if elect==4
(1,935 real changes made)

. 
. *****************************************************************************
> *************
. *******ANALYSIS 1************************************************************
> *************
. *****************************************************************************
> *************
. 
. //These are the main results reported in Table 1.2//
. 
. reg electability whitewoman blackwoman blackman, cluster(id) robust

Linear regression                               Number of obs     =      5,736
                                                F(3, 1911)        =       6.26
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0036
                                                Root MSE          =     .84619

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.0616255   .0311726    -1.98   0.048    -.1227614   -.0004896
  blackwoman |  -.1397379   .0334551    -4.18   0.000    -.2053501   -.0741256
    blackman |  -.0389426   .0326311    -1.19   0.233    -.1029388    .0250537
       _cons |   3.130615   .0230237   135.97   0.000     3.085461    3.175769
------------------------------------------------------------------------------

. reg veryelect whitewoman blackwoman blackman, cluster(id) robust

Linear regression                               Number of obs     =      5,736
                                                F(3, 1911)        =       5.14
                                                Prob > F          =     0.0015
                                                R-squared         =     0.0028
                                                Root MSE          =     .47231

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.0442949   .0181283    -2.44   0.015    -.0798483   -.0087416
  blackwoman |  -.0658708   .0180497    -3.65   0.000      -.10127   -.0304717
    blackman |  -.0198354   .0184849    -1.07   0.283    -.0560882    .0164173
       _cons |   .3697305   .0134776    27.43   0.000     .3432981    .3961628
------------------------------------------------------------------------------

. 
. *****************************************************************************
> *************
. *******ANALYSIS 2************************************************************
> *************
. *****************************************************************************
> ************* 
. 
. //This section provides the analysis for the Appendix tables and additional d
> escription//
. //of the findings in the manuscript.//
. 
. //APPENDIX TABLE 1.1//
. //add fixed effects by profile//
. reg electability whitewoman blackwoman blackman profile2 profile3, cluster(id
> )

Linear regression                               Number of obs     =      5,736
                                                F(5, 1911)        =     119.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0859
                                                Root MSE          =     .81066

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.0608799   .0297252    -2.05   0.041    -.1191771   -.0025827
  blackwoman |  -.1430782   .0323602    -4.42   0.000    -.2065433   -.0796131
    blackman |  -.0400989   .0311393    -1.29   0.198    -.1011696    .0209717
    profile2 |  -.1174492   .0196165    -5.99   0.000    -.1559213   -.0789771
    profile3 |  -.5641177   .0243476   -23.17   0.000    -.6118683   -.5163671
       _cons |   3.358735   .0246258   136.39   0.000     3.310439    3.407032
------------------------------------------------------------------------------

. estimates store one

. reg veryelect whitewoman blackwoman blackman profile2 profile3, cluster(id)

Linear regression                               Number of obs     =      5,736
                                                F(5, 1911)        =      85.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0525
                                                Root MSE          =     .46046

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.0442969   .0176051    -2.52   0.012     -.078824   -.0097697
  blackwoman |  -.0670271   .0176006    -3.81   0.000    -.1015454   -.0325088
    blackman |  -.0202432   .0179242    -1.13   0.259    -.0553963    .0149098
    profile2 |  -.0851007   .0127251    -6.69   0.000    -.1100573    -.060144
    profile3 |  -.2538139   .0128773   -19.71   0.000     -.279069   -.2285588
       _cons |   .4830913   .0155138    31.14   0.000     .4526656     .513517
------------------------------------------------------------------------------

. estimates store two

. esttab one two using profilefe, se rtf label addnotes("Standard errors cluste
> red by subject") replace
(output written to profilefe.rtf)

. 
. 
. //APPENDIX TABLE 1.2//
. //probit results for the "very electable" DV//
. probit veryelect whitewoman blackwoman blackman, cluster(id)

Iteration 0:   log pseudolikelihood = -3666.7806  
Iteration 1:   log pseudolikelihood = -3658.8413  
Iteration 2:   log pseudolikelihood = -3658.8407  
Iteration 3:   log pseudolikelihood = -3658.8407  

Probit regression                               Number of obs     =      5,736
                                                Wald chi2(3)      =      15.26
                                                Prob > chi2       =     0.0016
Log pseudolikelihood = -3658.8407               Pseudo R2         =     0.0022

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.1199851   .0491761    -2.44   0.015    -.2163685   -.0236018
  blackwoman |  -.1807645   .0496626    -3.64   0.000    -.2781013   -.0834276
    blackman |  -.0530366   .0494274    -1.07   0.283    -.1499125    .0438393
       _cons |  -.3325673   .0356948    -9.32   0.000    -.4025278   -.2626067
------------------------------------------------------------------------------

. estimates store probit

. //ordered probit results for "electability" DV//
. oprobit electability whitewoman blackwoman blackman, cluster(id)

Iteration 0:   log pseudolikelihood =  -6739.636  
Iteration 1:   log pseudolikelihood =  -6729.024  
Iteration 2:   log pseudolikelihood = -6729.0238  

Ordered probit regression                       Number of obs     =      5,736
                                                Wald chi2(3)      =      19.53
                                                Prob > chi2       =     0.0002
Log pseudolikelihood = -6729.0238               Pseudo R2         =     0.0016

                                 (Std. Err. adjusted for 1,912 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  whitewoman |  -.0886588   .0410768    -2.16   0.031    -.1691679   -.0081498
  blackwoman |  -.1824264   .0431695    -4.23   0.000    -.2670371   -.0978158
    blackman |  -.0523452   .0429566    -1.22   0.223    -.1365387    .0318483
-------------+----------------------------------------------------------------
       /cut1 |  -1.641953   .0412126                     -1.722728   -1.561178
       /cut2 |  -.8996479   .0328535                     -.9640396   -.8352562
       /cut3 |   .3401764   .0326763                      .2761321    .4042208
------------------------------------------------------------------------------

. estimates store ordprobit

. esttab ordprobit probit using probits, se rtf label addnotes("Standard errors
>  clustered by subject") dr(cut1 cut2 cut3) replace
(output written to probits.rtf)

. 
. //APPENDIX TABLE 1.3//
. //Welch's t-tests comparing black and/or female profiles vs. white male profi
> les //
. ttest electability, by(blackmancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    3.130615    .0221041    .8408295    3.087255    3.173975
       1 |   1,429    3.091672    .0224556    .8488709    3.047623    3.135722
---------+--------------------------------------------------------------------
combined |   2,876    3.111266     .015755    .8449121    3.080374    3.142158
---------+--------------------------------------------------------------------
    diff |            .0389426    .0315095                -.022841    .1007261
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.2359
Ho: diff = 0                             Welch's degrees of freedom =   2874.6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8917         Pr(|T| > |t|) = 0.2166          Pr(T > t) = 0.1083

. ttest veryelect, by(blackmancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    .3697305    .0126947    .4828985    .3448285    .3946324
       1 |   1,429     .349895    .0126211    .4771035    .3251372    .3746529
---------+--------------------------------------------------------------------
combined |   2,876    .3598748    .0089514    .4800469    .3423231    .3774266
---------+--------------------------------------------------------------------
    diff |            .0198354     .017901               -.0152647    .0549356
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.1081
Ho: diff = 0                             Welch's degrees of freedom =     2876

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8660         Pr(|T| > |t|) = 0.2679          Pr(T > t) = 0.1340

. 
. ttest electability, by(blackwomancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    3.130615    .0221041    .8408295    3.087255    3.173975
       1 |   1,425    2.990877    .0231009    .8720381    2.945562    3.036193
---------+--------------------------------------------------------------------
combined |   2,872    3.061281    .0160317    .8591537    3.029847    3.092716
---------+--------------------------------------------------------------------
    diff |            .1397379    .0319725                .0770464    .2024294
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   4.3706
Ho: diff = 0                             Welch's degrees of freedom =  2864.33

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest veryelect, by(blackwomancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    .3697305    .0126947    .4828985    .3448285    .3946324
       1 |   1,425    .3038596    .0121879    .4600842    .2799514    .3277679
---------+--------------------------------------------------------------------
combined |   2,872    .3370474    .0088221    .4727835    .3197491    .3543456
---------+--------------------------------------------------------------------
    diff |            .0658708    .0175983                .0313642    .1003774
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.7430
Ho: diff = 0                             Welch's degrees of freedom =  2868.86

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9999         Pr(|T| > |t|) = 0.0002          Pr(T > t) = 0.0001

. 
. ttest electability, by(whitewomancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    3.130615    .0221041    .8408295    3.087255    3.173975
       1 |   1,435     3.06899    .0217132    .8225255    3.026397    3.111583
---------+--------------------------------------------------------------------
combined |   2,882    3.099931    .0155016    .8321924    3.069535    3.130326
---------+--------------------------------------------------------------------
    diff |            .0616255    .0309848                 .000871      .12238
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.9889
Ho: diff = 0                             Welch's degrees of freedom =  2881.46

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9766         Pr(|T| > |t|) = 0.0468          Pr(T > t) = 0.0234

. ttest veryelect, by(whitewomancompare) w

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,447    .3697305    .0126947    .4828985    .3448285    .3946324
       1 |   1,435    .3254355    .0123728    .4687007    .3011647    .3497064
---------+--------------------------------------------------------------------
combined |   2,882    .3476752    .0088725    .4763149    .3302781    .3650724
---------+--------------------------------------------------------------------
    diff |            .0442949    .0177269                .0095363    .0790536
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.4987
Ho: diff = 0                             Welch's degrees of freedom =  2880.67

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9937         Pr(|T| > |t|) = 0.0125          Pr(T > t) = 0.0063

. 
. 
. **QUALITY CHECK -- are Experiment 1 results driven by low-quality responses?*
> *
. 
. //The survey included a screener question intended to gauge if the respondent
>  was//
. //paying attention, reading questions thoroughly, and following instructions.
> //
. 
. //Respondents who answered the screener question correctly are coded as "high
>  quality."//
. 
. //Appendix Table 1.4//
. reg electability blackman whitewoman blackwoman if highquality==1, cluster(id
> )

Linear regression                               Number of obs     =      3,762
                                                F(3, 1253)        =       8.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0072
                                                Root MSE          =     .84424

                                 (Std. Err. adjusted for 1,254 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0912056   .0399248    -2.28   0.023    -.1695324   -.0128788
  whitewoman |  -.1015261   .0378172    -2.68   0.007    -.1757182    -.027334
  blackwoman |  -.2038264   .0409683    -4.98   0.000    -.2842005   -.1234523
       _cons |   3.140351   .0276007   113.78   0.000     3.086202    3.194499
------------------------------------------------------------------------------

. estimates store highqual1

. reg veryelect blackman whitewoman blackwoman if highquality==1, cluster(id)

Linear regression                               Number of obs     =      3,762
                                                F(3, 1253)        =       4.65
                                                Prob > F          =     0.0031
                                                R-squared         =     0.0038
                                                Root MSE          =     .46451

                                 (Std. Err. adjusted for 1,254 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0359031   .0224065    -1.60   0.109    -.0798614    .0080553
  whitewoman |  -.0500532   .0221514    -2.26   0.024     -.093511   -.0065953
  blackwoman |  -.0796765   .0218267    -3.65   0.000    -.1224974   -.0368555
       _cons |   .3585526   .0164803    21.76   0.000     .3262205    .3908847
------------------------------------------------------------------------------

. estimates store highqual2

. esttab highqual1 highqual2 using highqual, se rtf label addnotes("Standard er
> rors clustered by subject") replace
(output written to highqual.rtf)

. 
. //using only data from subjects who passed the screener question, the results
>  are similar//
. //except that the results for the black male candidates are statistically sig
> nificant in one//
. //model.//
. 
. **EXTERNAL VALIDITY CHECK -- are the results being driven by people who are**
. **not politically engaged?**
. 
. //Unfortunately I do not have direct measures of voter registration, intentio
> ns to vote//
. //or past voting behavior of the subjects.//
. 
. //However, there are several measures of political knowledge and engagement i
> n the survey.//
. 
. //The analysis below produces APPENDIX TABLES 1.5 to 1.13//
. 
. //The survey asked subjects if they are liberal, cons, moderate, or if they//
. //"haven't given much thought" to their ideology.//
. //Let's repeat the basic models excluding people who said "haven't given it m
> uch thought."//
. 
. reg electability blackman whitewoman blackwoman if noideology==0, cluster(id)

Linear regression                               Number of obs     =      4,953
                                                F(3, 1650)        =       8.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0056
                                                Root MSE          =      .8409

                                 (Std. Err. adjusted for 1,651 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0648145   .0347644    -1.86   0.062    -.1330014    .0033724
  whitewoman |  -.0849489   .0327022    -2.60   0.009     -.149091   -.0208067
  blackwoman |  -.1771884   .0357827    -4.95   0.000    -.2473726   -.1070042
       _cons |   3.175566   .0242187   131.12   0.000     3.128064    3.223069
------------------------------------------------------------------------------

. estimates store noideol1

. reg veryelect blackman whitewoman blackwoman if noideology==0, cluster(id) 

Linear regression                               Number of obs     =      4,953
                                                F(3, 1650)        =       6.07
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0038
                                                Root MSE          =     .47648

                                 (Std. Err. adjusted for 1,651 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0286102   .0200622    -1.43   0.154    -.0679602    .0107399
  whitewoman |  -.0523645   .0195687    -2.68   0.008    -.0907466   -.0139824
  blackwoman |  -.0793412   .0195696    -4.05   0.000     -.117725   -.0409574
       _cons |   .3907767   .0146327    26.71   0.000      .362076    .4194774
------------------------------------------------------------------------------

. estimates store noideol2

. esttab noideol1 noideol2 using noideol, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to noideol.rtf)

. 
. //There were also 2 questions about gov knowledge -- do subjects know who det
> ermines if laws//
. //are constitutional, and do they know who nominates federal judges?//
. 
. //Now let's repeat the regs only with those who answered both questions right
> .//
. 
. reg electability blackman whitewoman blackwoman if nominatejudges2==1 & const
> itutional2==1, cluster(id)

Linear regression                               Number of obs     =      2,355
                                                F(3, 784)         =      10.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0143
                                                Root MSE          =     .81912

                                   (Std. Err. adjusted for 785 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1311919   .0493049    -2.66   0.008    -.2279771   -.0344067
  whitewoman |  -.1299463   .0481711    -2.70   0.007    -.2245058   -.0353868
  blackwoman |  -.2840552   .0518205    -5.48   0.000    -.3857786   -.1823318
       _cons |   3.190305   .0340869    93.59   0.000     3.123393    3.257218
------------------------------------------------------------------------------

. estimates store govknow1

. reg veryelect blackman whitewoman blackwoman if nominatejudges2==1 & constitu
> tional2==1, cluster(id) 

Linear regression                               Number of obs     =      2,355
                                                F(3, 784)         =       6.01
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0076
                                                Root MSE          =     .46318

                                   (Std. Err. adjusted for 785 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0581581   .0292242    -1.99   0.047    -.1155251   -.0007912
  whitewoman |  -.0569527   .0289613    -1.97   0.050    -.1138036   -.0001019
  blackwoman |  -.1164846   .0278297    -4.19   0.000    -.1711141   -.0618552
       _cons |   .3734291   .0216905    17.22   0.000     .3308507    .4160074
------------------------------------------------------------------------------

. estimates store govknow2

. esttab govknow1 govknow2 using govknow, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to govknow.rtf)

. 
. //Could some people have guessed the right answers randomly? Yes. So let's re
> peat the main models including only//
. //subjects who passed the attention check question AND answered the two const
> itutional knowledge questions correctly.//
. 
. reg electability blackman whitewoman blackwoman if nominatejudges2==1 & const
> itutional2==1 & highqual==1, cluster(id)

Linear regression                               Number of obs     =      1,806
                                                F(3, 601)         =      13.21
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0239
                                                Root MSE          =     .81904

                                   (Std. Err. adjusted for 602 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.2172814   .0560671    -3.88   0.000    -.3273927   -.1071701
  whitewoman |  -.2059874   .0552997    -3.72   0.000    -.3145914   -.0973833
  blackwoman |  -.3719356   .0596223    -6.24   0.000    -.4890291   -.2548421
       _cons |   3.261307   .0386542    84.37   0.000     3.185393     3.33722
------------------------------------------------------------------------------

. estimates store govknowhighqual1

. reg veryelect blackman whitewoman blackwoman if nominatejudges2==1 & constitu
> tional2==1 & highqual==1, cluster(id) 

Linear regression                               Number of obs     =      1,806
                                                F(3, 601)         =       7.17
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0129
                                                Root MSE          =     .46377

                                   (Std. Err. adjusted for 602 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1055645   .0339706    -3.11   0.002    -.1722801   -.0388489
  whitewoman |  -.0903988   .0338954    -2.67   0.008    -.1569666    -.023831
  blackwoman |  -.1514132   .0326911    -4.63   0.000    -.2156159   -.0872106
       _cons |   .4095477   .0259488    15.78   0.000     .3585864    .4605091
------------------------------------------------------------------------------

. estimates store govknowhighqual2

. esttab govknowhighqual1 govknowhighqual2 using govknowhighqual, se rtf label 
> addnotes("Standard errors clustered by subject") replace
(output written to govknowhighqual.rtf)

. 
. //There were also questions about ID'ing Nancy Pelosi and Steve Mnuchin.//
. //I have some concerns about these (do they introduce a filter for partisansh
> ip?) but let's go ahead and use them.//
. 
. reg electability blackman whitewoman blackwoman if pelosi2==1 & mnuchin2==1, 
> cluster(id)

Linear regression                               Number of obs     =      1,362
                                                F(3, 453)         =       5.20
                                                Prob > F          =     0.0015
                                                R-squared         =     0.0130
                                                Root MSE          =     .79351

                                   (Std. Err. adjusted for 454 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1064175   .0657755    -1.62   0.106    -.2356805    .0228456
  whitewoman |  -.0291185   .0604814    -0.48   0.630    -.1479775    .0897404
  blackwoman |  -.2371137   .0654282    -3.62   0.000    -.3656941   -.1085333
       _cons |   3.201923   .0436698    73.32   0.000     3.116102    3.287744
------------------------------------------------------------------------------

. estimates store pelosimnuchin1

. reg veryelect blackman whitewoman blackwoman if pelosi2==1 & mnuchin2==1, clu
> ster(id)

Linear regression                               Number of obs     =      1,362
                                                F(3, 453)         =       2.16
                                                Prob > F          =     0.0914
                                                R-squared         =     0.0044
                                                Root MSE          =     .47131

                                   (Std. Err. adjusted for 454 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   -.022688    .039013    -0.58   0.561    -.0993569    .0539809
  whitewoman |  -.0169427   .0395589    -0.43   0.669    -.0946844     .060799
  blackwoman |  -.0838597   .0369139    -2.27   0.024    -.1564034    -.011316
       _cons |   .3653846   .0294258    12.42   0.000     .3075565    .4232127
------------------------------------------------------------------------------

. estimates store pelosimnuchin2

. esttab pelosimnuchin1 pelosimnuchin2 using pelosimnuchin, se rtf label addnot
> es("Standard errors clustered by subject") replace
(output written to pelosimnuchin.rtf)

. //I think the results change here because men are much more likely//
. //to correctly ID Mnuchin.//
. 
. //Now we'll include only those subjects who correctly id'ed Pelosi and Mnuchi
> n and passed the attention check//
. //question.//
. 
. reg electability blackman whitewoman blackwoman if pelosi2==1 & mnuchin2==1 &
>  highqual==1, cluster(id)

Linear regression                               Number of obs     =      1,041
                                                F(3, 346)         =       8.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0277
                                                Root MSE          =     .80418

                                   (Std. Err. adjusted for 347 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1828622   .0747417    -2.45   0.015    -.3298674    -.035857
  whitewoman |  -.0845865   .0692013    -1.22   0.222    -.2206947    .0515217
  blackwoman |   -.369403   .0737922    -5.01   0.000    -.5145406   -.2242653
       _cons |       3.25   .0485058    67.00   0.000     3.154597    3.345403
------------------------------------------------------------------------------

. estimates store pelosimnuchinqual1

. reg veryelect blackman whitewoman blackwoman if pelosi2==1 & mnuchin2==1 & hi
> ghqual==1, cluster(id)

Linear regression                               Number of obs     =      1,041
                                                F(3, 346)         =       4.18
                                                Prob > F          =     0.0064
                                                R-squared         =     0.0108
                                                Root MSE          =     .46769

                                   (Std. Err. adjusted for 347 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0482395   .0440035    -1.10   0.274    -.1347875    .0383084
  whitewoman |  -.0343045   .0455967    -0.75   0.452     -.123986     .055377
  blackwoman |  -.1339286   .0410925    -3.26   0.001    -.2147511   -.0531061
       _cons |   .3839286    .033997    11.29   0.000     .3170618    .4507954
------------------------------------------------------------------------------

. estimates store pelosimnuchinqual2

. esttab pelosimnuchinqual1 pelosimnuchinqual2 using pelosimnuchinqual, se rtf 
> label addnotes("Standard errors clustered by subject") replace
(output written to pelosimnuchinqual.rtf)

. 
. 
. //I also combined the gov and political knowledge questions into one variable
>  called "polknowledge."//
. //It's codes the number of correct answers to the Mnuchin, Pelosi, and consti
> tutional questions.//
. 
. //Let's see what happens if we look at the lowest-knowledge respondents only:
> //
. reg electability blackman whitewoman blackwoman if polknow==0, cluster(id)

Linear regression                               Number of obs     =        777
                                                F(3, 258)         =       0.66
                                                Prob > F          =     0.5760
                                                R-squared         =     0.0026
                                                Root MSE          =      .8547

                                   (Std. Err. adjusted for 259 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |    .009288   .0881197     0.11   0.916    -.1642375    .1828135
  whitewoman |  -.0454545   .0874263    -0.52   0.604    -.2176145    .1267054
  blackwoman |  -.0974524   .0884403    -1.10   0.272    -.2716091    .0767044
       _cons |   3.156566   .0647567    48.74   0.000     3.029047    3.284085
------------------------------------------------------------------------------

. estimates store lowknow1

. reg veryelect blackman whitewoman blackwoman if polknow==0, cluster(id)

Linear regression                               Number of obs     =        777
                                                F(3, 258)         =       0.73
                                                Prob > F          =     0.5359
                                                R-squared         =     0.0029
                                                Root MSE          =     .48388

                                   (Std. Err. adjusted for 259 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0036955    .050717    -0.07   0.942    -.1035674    .0961764
  whitewoman |  -.0196704   .0515818    -0.38   0.703    -.1212454    .0819046
  blackwoman |  -.0638901   .0509145    -1.25   0.211     -.164151    .0363708
       _cons |   .3939394   .0380883    10.34   0.000     .3189359    .4689429
------------------------------------------------------------------------------

. estimates store lowknow2

. esttab lowknow1 lowknow2 using know0, se rtf label addnotes("Standard errors 
> clustered by subject") replace
(output written to know0.rtf)

. 
. //Let's see what happens if we look only at subjects who said they "haven't g
> iven much thought" to their ideology//
. reg electability blackman whitewoman blackwoman if noideol==1, cluster(id)

Linear regression                               Number of obs     =        783
                                                F(3, 260)         =       0.44
                                                Prob > F          =     0.7251
                                                R-squared         =     0.0019
                                                Root MSE          =     .86244

                                   (Std. Err. adjusted for 261 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1014514   .0896777     1.13   0.259    -.0751357    .2780386
  whitewoman |   .0582333   .0933522     0.62   0.533    -.1255893     .242056
  blackwoman |   .0754098    .091322     0.83   0.410    -.1044151    .2552346
       _cons |   2.867299   .0654824    43.79   0.000     2.738355    2.996242
------------------------------------------------------------------------------

. estimates store noideol1

. reg veryelect blackman whitewoman blackwoman if noideol==1, cluster(id)

Linear regression                               Number of obs     =        783
                                                F(3, 260)         =       0.17
                                                Prob > F          =     0.9140
                                                R-squared         =     0.0007
                                                Root MSE          =     .43562

                                   (Std. Err. adjusted for 261 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0243878   .0456375     0.53   0.594    -.0654783     .114254
  whitewoman |  -.0070838    .047037    -0.15   0.880    -.0997058    .0855382
  blackwoman |   .0087628   .0461619     0.19   0.850     -.082136    .0996617
       _cons |   .2464455   .0328117     7.51   0.000      .181835     .311056
------------------------------------------------------------------------------

. estimates store noideol2

. esttab noideol1 noideol2 using noideol1, se rtf label addnotes("Standard erro
> rs clustered by subject") replace
(output written to noideol1.rtf)

. 
. //Let's see what happens if we look at the people who failed the attention ch
> eck question only.//
. reg electability blackman whitewoman blackwoman if highqual==0, cluster(id)

Linear regression                               Number of obs     =      1,974
                                                F(3, 657)         =       0.60
                                                Prob > F          =     0.6165
                                                R-squared         =     0.0010
                                                Root MSE          =     .84585

                                   (Std. Err. adjusted for 658 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0583951   .0561269     1.04   0.299    -.0518146    .1686048
  whitewoman |    .014612   .0545906     0.27   0.789    -.0925811    .1218051
  blackwoman |  -.0105704   .0570573    -0.19   0.853    -.1226072    .1014663
       _cons |   3.114019    .040836    76.26   0.000     3.033834    3.194204
------------------------------------------------------------------------------

. estimates store lowqual1

. reg veryelect blackman whitewoman blackwoman if highqual==0, cluster(id)

Linear regression                               Number of obs     =      1,974
                                                F(3, 657)         =       0.99
                                                Prob > F          =     0.3990
                                                R-squared         =     0.0016
                                                Root MSE          =     .48471

                                   (Std. Err. adjusted for 658 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0128377   .0323472     0.40   0.692    -.0506787     .076354
  whitewoman |  -.0298639   .0313517    -0.95   0.341    -.0914256    .0316978
  blackwoman |  -.0331816   .0319617    -1.04   0.300     -.095941    .0295778
       _cons |    .388785   .0232154    16.75   0.000     .3431997    .4343704
------------------------------------------------------------------------------

. estimates store lowqual2

. esttab lowqual1 lowqual2 using lowqual, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to lowqual.rtf)

. //These subjects are probably introducing "noise" into the overall results. T
> hey are certainly not driving my findings.//
. 
. //Now let's look at the more knowledgeable subjects only.//
. reg electability blackman whitewoman blackwoman if polknow>0, cluster(id)

Linear regression                               Number of obs     =      4,959
                                                F(3, 1652)        =       5.78
                                                Prob > F          =     0.0006
                                                R-squared         =     0.0039
                                                Root MSE          =     .84485

                                 (Std. Err. adjusted for 1,653 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0472528   .0351624    -1.34   0.179    -.1162204    .0217148
  whitewoman |  -.0632101    .033373    -1.89   0.058    -.1286678    .0022477
  blackwoman |  -.1469595   .0361209    -4.07   0.000    -.2178071   -.0761118
       _cons |   3.126501   .0246264   126.96   0.000     3.078199    3.174803
------------------------------------------------------------------------------

. reg veryelect blackman whitewoman blackwoman if polknow>0, cluster(id)

Linear regression                               Number of obs     =      4,959
                                                F(3, 1652)        =       4.52
                                                Prob > F          =     0.0037
                                                R-squared         =     0.0028
                                                Root MSE          =     .47043

                                 (Std. Err. adjusted for 1,653 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0227555   .0198728    -1.15   0.252    -.0617339     .016223
  whitewoman |  -.0470636   .0193444    -2.43   0.015    -.0850057   -.0091215
  blackwoman |  -.0663837   .0192727    -3.44   0.001    -.1041852   -.0285823
       _cons |   .3658927   .0143999    25.41   0.000     .3376487    .3941367
------------------------------------------------------------------------------

. 
. reg electability blackman whitewoman blackwoman if polknow>1, cluster(id)

Linear regression                               Number of obs     =      3,633
                                                F(3, 1210)        =       6.36
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0058
                                                Root MSE          =     .82973

                                 (Std. Err. adjusted for 1,211 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   -.060758   .0406271    -1.50   0.135    -.1404654    .0189493
  whitewoman |   -.061223   .0387202    -1.58   0.114    -.1371891    .0147432
  blackwoman |  -.1755218   .0411178    -4.27   0.000    -.2561918   -.0948517
       _cons |   3.139298   .0283423   110.76   0.000     3.083692    3.194903
------------------------------------------------------------------------------

. reg veryelect blackman whitewoman blackwoman if polknow>1, cluster(id)

Linear regression                               Number of obs     =      3,633
                                                F(3, 1210)        =       4.24
                                                Prob > F          =     0.0054
                                                R-squared         =     0.0034
                                                Root MSE          =     .46776

                                 (Std. Err. adjusted for 1,211 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0282775   .0235363    -1.20   0.230    -.0744541    .0178991
  whitewoman |  -.0350022   .0230636    -1.52   0.129    -.0802514     .010247
  blackwoman |  -.0769306   .0221603    -3.47   0.001    -.1204074   -.0334538
       _cons |   .3601359   .0170554    21.12   0.000     .3266744    .3935974
------------------------------------------------------------------------------

. 
. reg electability blackman whitewoman blackwoman if polknow>2, cluster(id)

Linear regression                               Number of obs     =      2,352
                                                F(3, 783)         =       7.94
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0112
                                                Root MSE          =     .81416

                                   (Std. Err. adjusted for 784 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0792933   .0495066    -1.60   0.110    -.1764746     .017888
  whitewoman |  -.0888295    .047465    -1.87   0.062    -.1820032    .0043442
  blackwoman |   -.240902   .0503046    -4.79   0.000    -.3396498   -.1421542
       _cons |   3.174688   .0338352    93.83   0.000      3.10827    3.241107
------------------------------------------------------------------------------

. reg veryelect blackman whitewoman blackwoman if polknow>2, cluster(id)

Linear regression                               Number of obs     =      2,352
                                                F(3, 783)         =       4.61
                                                Prob > F          =     0.0033
                                                R-squared         =     0.0055
                                                Root MSE          =     .46679

                                   (Std. Err. adjusted for 784 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0212555   .0290786    -0.73   0.465    -.0783368    .0358258
  whitewoman |  -.0317885   .0292561    -1.09   0.278    -.0892182    .0256413
  blackwoman |  -.0935178   .0275525    -3.39   0.001    -.1476034   -.0394322
       _cons |   .3600713   .0216413    16.64   0.000     .3175895    .4025531
------------------------------------------------------------------------------

. 
. reg electability blackman whitewoman blackwoman if polknow>3, cluster(id)

Linear regression                               Number of obs     =        969
                                                F(3, 322)         =       5.80
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0213
                                                Root MSE          =     .78132

                                   (Std. Err. adjusted for 323 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   -.180306   .0766224    -2.35   0.019    -.3310497   -.0295622
  whitewoman |  -.0525707   .0709105    -0.74   0.459    -.1920772    .0869358
  blackwoman |  -.3045315   .0802047    -3.80   0.000    -.4623229     -.14674
       _cons |   3.221198   .0492107    65.46   0.000     3.124383    3.318013
------------------------------------------------------------------------------

. reg veryelect blackman whitewoman blackwoman if polknow>3, cluster(id)

Linear regression                               Number of obs     =        969
                                                F(3, 322)         =       3.02
                                                Prob > F          =     0.0302
                                                R-squared         =     0.0088
                                                Root MSE          =     .46375

                                   (Std. Err. adjusted for 323 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0508968   .0457817    -1.11   0.267    -.1409659    .0391722
  whitewoman |  -.0104274   .0471881    -0.22   0.825    -.1032634    .0824086
  blackwoman |   -.113833    .043998    -2.59   0.010    -.2003927   -.0272732
       _cons |    .359447   .0344264    10.44   0.000     .2917179    .4271761
------------------------------------------------------------------------------

. 
. //Now let's exclude people with no ideology, those who did not pass the atten
> tion check//
. //and those with very low political knowledge.//
. 
. //Essentially, we are left with reasonably knowledgeable, political, and atte
> ntive respondents only://
. reg electability blackman whitewoman blackwoman if polknow>0 & noideol==0 & h
> ighquality==1, cluster(id)

Linear regression                               Number of obs     =      3,030
                                                F(3, 1009)        =       9.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0106
                                                Root MSE          =     .84055

                                 (Std. Err. adjusted for 1,010 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   -.109626   .0448557    -2.44   0.015    -.1976472   -.0216048
  whitewoman |  -.1157186   .0421211    -2.75   0.006    -.1983736   -.0330636
  blackwoman |  -.2470642   .0458803    -5.38   0.000     -.337096   -.1570324
       _cons |   3.168759   .0308524   102.71   0.000     3.108216    3.229301
------------------------------------------------------------------------------

. estimates store higherqual1

. reg veryelect blackman whitewoman blackwoman if polknow>0 & noideol==0 & high
> quality==1, cluster(id)

Linear regression                               Number of obs     =      3,030
                                                F(3, 1009)        =       6.13
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0061
                                                Root MSE          =      .4663

                                 (Std. Err. adjusted for 1,010 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0454258   .0252112    -1.80   0.072    -.0948981    .0040465
  whitewoman |  -.0647967   .0248085    -2.61   0.009    -.1134788   -.0161146
  blackwoman |  -.1018579   .0244331    -4.17   0.000    -.1498034   -.0539123
       _cons |    .376569   .0186828    20.16   0.000     .3399074    .4132307
------------------------------------------------------------------------------

. estimates store higherqual2

. esttab higherqual1 higherqual2 using higherqual, se rtf label addnotes("Stand
> ard errors clustered by subject") replace
(output written to higherqual.rtf)

. 
. //Now let's have a look at the most knowledgeble, attentive, and political re
> spondents only://
. reg electability blackman whitewoman blackwoman if polknow>2 & noideol==0 & h
> ighquality==1, cluster(id)

Linear regression                               Number of obs     =      1,707
                                                F(3, 568)         =      10.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0213
                                                Root MSE          =     .81434

                                   (Std. Err. adjusted for 569 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1605915   .0583783    -2.75   0.006    -.2752553   -.0459278
  whitewoman |  -.1457741   .0547086    -2.66   0.008    -.2532299   -.0383183
  blackwoman |   -.344108    .060158    -5.72   0.000    -.4622672   -.2259488
       _cons |   3.228723   .0395652    81.61   0.000     3.151011    3.306435
------------------------------------------------------------------------------

. estimates store veryhighestqual1

. reg veryelect blackman whitewoman blackwoman if polknow>2 & noideol==0 & high
> quality==1, cluster(id)

Linear regression                               Number of obs     =      1,707
                                                F(3, 568)         =       5.63
                                                Prob > F          =     0.0008
                                                R-squared         =     0.0097
                                                Root MSE          =     .46379

                                   (Std. Err. adjusted for 569 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0620996   .0343637    -1.81   0.071     -.129595    .0053958
  whitewoman |  -.0580939   .0347175    -1.67   0.095    -.1262843    .0100965
  blackwoman |  -.1318475   .0328782    -4.01   0.000    -.1964251   -.0672699
       _cons |   .3829787   .0264609    14.47   0.000     .3310055    .4349519
------------------------------------------------------------------------------

. estimates store veryhighestqual2

. esttab veryhighestqual1 veryhighestqual2 using highestqual2, se rtf label add
> notes("Standard errors clustered by subject") replace
(output written to highestqual2.rtf)

. //Wow.//
. 
. //SUB-GROUP ANALYSIS//
. 
. //The code below produces the sub-group analysis cited in manuscript and rela
> ted Appendix tables.//
. 
. //APPENDIX TABLE 1.15//
. reg electability blackman whitewoman blackwoman if education<3, cluster(id)

Linear regression                               Number of obs     =      1,752
                                                F(3, 583)         =       2.22
                                                Prob > F          =     0.0847
                                                R-squared         =     0.0042
                                                Root MSE          =     .85034

                                   (Std. Err. adjusted for 584 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1073458   .0581546    -1.85   0.065    -.2215638    .0068722
  whitewoman |  -.0541217   .0561343    -0.96   0.335    -.1643718    .0561284
  blackwoman |  -.1466734   .0602934    -2.43   0.015    -.2650922   -.0282546
       _cons |   3.095982   .0405758    76.30   0.000      3.01629    3.175675
------------------------------------------------------------------------------

. estimates store educ1

. reg electability blackman whitewoman blackwoman if education<5 & education>2,
>  cluster(id)

Linear regression                               Number of obs     =      2,235
                                                F(3, 744)         =       1.68
                                                Prob > F          =     0.1689
                                                R-squared         =     0.0025
                                                Root MSE          =     .86948

                                   (Std. Err. adjusted for 745 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0049171   .0542805     0.09   0.928    -.1016441    .1114783
  whitewoman |  -.0582894   .0510509    -1.14   0.254    -.1585103    .0419316
  blackwoman |  -.1000009   .0547248    -1.83   0.068    -.2074344    .0074325
       _cons |    3.06175   .0370927    82.54   0.000     2.988931    3.134568
------------------------------------------------------------------------------

. estimates store educ2

. reg electability blackman whitewoman blackwoman if education>4, cluster(id)

Linear regression                               Number of obs     =      1,749
                                                F(3, 582)         =       4.15
                                                Prob > F          =     0.0063
                                                R-squared         =     0.0081
                                                Root MSE          =     .80053

                                   (Std. Err. adjusted for 583 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0467507   .0564885    -0.83   0.408    -.1576968    .0641954
  whitewoman |  -.0794114    .054072    -1.47   0.142    -.1856115    .0267887
  blackwoman |  -.1965498   .0587235    -3.35   0.001    -.3118855    -.081214
       _cons |   3.264423    .041549    78.57   0.000     3.182819    3.346027
------------------------------------------------------------------------------

. estimates store educ3

. esttab educ1 educ2 educ3 using educelect, se rtf label addnotes("Standard err
> ors clustered by subject") replace
(output written to educelect.rtf)

. 
. //APPENDIX TABLE 1.14//
. reg veryelect blackman whitewoman blackwoman if education<3, cluster(id)

Linear regression                               Number of obs     =      1,752
                                                F(3, 583)         =       1.73
                                                Prob > F          =     0.1594
                                                R-squared         =     0.0034
                                                Root MSE          =     .46077

                                   (Std. Err. adjusted for 584 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0572646    .032878    -1.74   0.082    -.1218384    .0073092
  whitewoman |  -.0481208   .0320742    -1.50   0.134    -.1111158    .0148741
  blackwoman |  -.0716446   .0328305    -2.18   0.029     -.136125   -.0071642
       _cons |   .3504464   .0237616    14.75   0.000     .3037776    .3971152
------------------------------------------------------------------------------

. estimates store educ4

. reg veryelect blackman whitewoman blackwoman if education<5 & education>2, cl
> uster(id)

Linear regression                               Number of obs     =      2,235
                                                F(3, 744)         =       1.57
                                                Prob > F          =     0.1965
                                                R-squared         =     0.0021
                                                Root MSE          =     .46619

                                   (Std. Err. adjusted for 745 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0247652   .0293401     0.84   0.399    -.0328341    .0823645
  whitewoman |  -.0283078   .0282556    -1.00   0.317     -.083778    .0271623
  blackwoman |  -.0270693   .0277638    -0.97   0.330     -.081574    .0274354
       _cons |   .3276158   .0209092    15.67   0.000     .2865678    .3686637
------------------------------------------------------------------------------

. estimates store educ5

. reg veryelect blackman whitewoman blackwoman if education>4, cluster(id)

Linear regression                               Number of obs     =      1,749
                                                F(3, 582)         =       4.08
                                                Prob > F          =     0.0070
                                                R-squared         =     0.0072
                                                Root MSE          =     .48679

                                   (Std. Err. adjusted for 583 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0486572   .0340005    -1.43   0.153    -.1154358    .0181214
  whitewoman |  -.0654443   .0344886    -1.90   0.058    -.1331816     .002293
  blackwoman |    -.11694   .0339942    -3.44   0.001    -.1837063   -.0501737
       _cons |   .4495192    .025608    17.55   0.000     .3992238    .4998146
------------------------------------------------------------------------------

. estimates store educ6

. esttab educ4 educ5 educ6 using educvery, se rtf label addnotes("Standard erro
> rs clustered by subject") replace
(output written to educvery.rtf)

. 
. //Now let's look at generational differences.//
. 
. //APPENDIX TABLE 1.17//
. reg electability blackman whitewoman blackwoman if age<26, cluster(id)

Linear regression                               Number of obs     =        774
                                                F(3, 257)         =       1.19
                                                Prob > F          =     0.3153
                                                R-squared         =     0.0051
                                                Root MSE          =     .85963

                                   (Std. Err. adjusted for 258 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0569885   .0968122    -0.59   0.557    -.2476346    .1336577
  whitewoman |  -.0740245   .0898112    -0.82   0.411    -.2508842    .1028351
  blackwoman |   -.167132   .0938831    -1.78   0.076    -.3520102    .0177461
       _cons |   3.130435   .0709919    44.10   0.000     2.990635    3.270235
------------------------------------------------------------------------------

. estimates store genz1

. reg electability blackman whitewoman blackwoman if age>25 & age<40, cluster(i
> d)

Linear regression                               Number of obs     =      1,659
                                                F(3, 552)         =       1.69
                                                Prob > F          =     0.1676
                                                R-squared         =     0.0030
                                                Root MSE          =     .88276

                                   (Std. Err. adjusted for 553 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0988272   .0621136     1.59   0.112    -.0231807     .220835
  whitewoman |   -.022283   .0610537    -0.36   0.715     -.142209    .0976431
  blackwoman |  -.0119375   .0677816    -0.18   0.860    -.1450789     .121204
       _cons |   3.058691   .0449937    67.98   0.000     2.970311    3.147071
------------------------------------------------------------------------------

. estimates store mil1

. reg electability blackman whitewoman blackwoman if age>39 & age<=54, cluster(
> id)

Linear regression                               Number of obs     =      1,398
                                                F(3, 465)         =       5.58
                                                Prob > F          =     0.0009
                                                R-squared         =     0.0126
                                                Root MSE          =     .83554

                                   (Std. Err. adjusted for 466 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1961894   .0646092    -3.04   0.003    -.3231516   -.0692271
  whitewoman |  -.1188255   .0614598    -1.93   0.054    -.2395988    .0019479
  blackwoman |  -.2506306   .0652166    -3.84   0.000    -.3787863   -.1224749
       _cons |   3.264957   .0424026    77.00   0.000     3.181633    3.348282
------------------------------------------------------------------------------

. estimates store genx1

. reg electability blackman whitewoman blackwoman if age>=55 & age<71, cluster(
> id)

Linear regression                               Number of obs     =      1,422
                                                F(3, 473)         =       1.69
                                                Prob > F          =     0.1690
                                                R-squared         =     0.0040
                                                Root MSE          =     .80228

                                   (Std. Err. adjusted for 474 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0310803   .0633353    -0.49   0.624    -.1555335     .093373
  whitewoman |  -.0316503   .0570355    -0.55   0.579    -.1437247     .080424
  blackwoman |  -.1342977   .0622603    -2.16   0.032    -.2566387   -.0119568
       _cons |   3.106443   .0429781    72.28   0.000     3.021991    3.190894
------------------------------------------------------------------------------

. estimates store boom1

. reg electability blackman whitewoman blackwoman if age>70, cluster(id)

Linear regression                               Number of obs     =        483
                                                F(3, 160)         =       1.69
                                                Prob > F          =     0.1712
                                                R-squared         =     0.0096
                                                Root MSE          =     .84169

                                   (Std. Err. adjusted for 161 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0570401   .1031683    -0.55   0.581    -.2607873    .1467072
  whitewoman |  -.1138015   .1114094    -1.02   0.309     -.333824    .1062211
  blackwoman |  -.2293233    .107455    -2.13   0.034    -.4415363   -.0171103
       _cons |   3.071429   .0771056    39.83   0.000     2.919153    3.223705
------------------------------------------------------------------------------

. estimates store silent1

. esttab genz1 mil1 genx1 boom1 silent1 using genelect, se rtf label addnotes("
> Standard errors clustered by subject") replace
(output written to genelect.rtf)

. 
. //APPENDIX TABLE 1.16//
. reg veryelect blackman whitewoman blackwoman if age<26, cluster(id)

Linear regression                               Number of obs     =        774
                                                F(3, 257)         =       2.99
                                                Prob > F          =     0.0318
                                                R-squared         =     0.0131
                                                Root MSE          =     .46767

                                   (Std. Err. adjusted for 258 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0684107   .0511727    -1.34   0.182     -.169182    .0323605
  whitewoman |  -.1207358   .0508762    -2.37   0.018    -.2209231   -.0205485
  blackwoman |  -.1378141   .0497144    -2.77   0.006    -.2357136   -.0399147
       _cons |   .4130435   .0378129    10.92   0.000     .3385809    .4875061
------------------------------------------------------------------------------

. estimates store genz2

. reg veryelect blackman whitewoman blackwoman if age>25 & age<40, cluster(id)

Linear regression                               Number of obs     =      1,659
                                                F(3, 552)         =       1.32
                                                Prob > F          =     0.2660
                                                R-squared         =     0.0024
                                                Root MSE          =     .48048

                                   (Std. Err. adjusted for 553 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0348783   .0337665     1.03   0.302    -.0314483    .1012049
  whitewoman |  -.0312466   .0328346    -0.95   0.342    -.0957427    .0332495
  blackwoman |   .0073173    .034467     0.21   0.832    -.0603851    .0750197
       _cons |   .3589165   .0239143    15.01   0.000     .3119424    .4058906
------------------------------------------------------------------------------

. estimates store mil2

. reg veryelect blackman whitewoman blackwoman if age>39 & age<=54, cluster(id)

Linear regression                               Number of obs     =      1,398
                                                F(3, 465)         =       3.10
                                                Prob > F          =     0.0267
                                                R-squared         =     0.0071
                                                Root MSE          =     .48056

                                   (Std. Err. adjusted for 466 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0749149   .0380783    -1.97   0.050    -.1497417   -.0000881
  whitewoman |  -.0605882   .0393176    -1.54   0.124    -.1378504    .0166739
  blackwoman |  -.1121642   .0374397    -3.00   0.003    -.1857362   -.0385922
       _cons |   .4273504   .0286172    14.93   0.000     .3711154    .4835854
------------------------------------------------------------------------------

. estimates store genx2

. reg veryelect blackman whitewoman blackwoman if age>=55 & age<71, cluster(id)

Linear regression                               Number of obs     =      1,422
                                                F(3, 473)         =       1.05
                                                Prob > F          =     0.3700
                                                R-squared         =     0.0021
                                                Root MSE          =     .46096

                                   (Std. Err. adjusted for 474 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0024114   .0374295     0.06   0.949    -.0711372      .07596
  whitewoman |  -.0063083   .0347606    -0.18   0.856    -.0746126    .0619959
  blackwoman |  -.0491327   .0344721    -1.43   0.155    -.1168702    .0186047
       _cons |   .3193277   .0266234    11.99   0.000     .2670129    .3716425
------------------------------------------------------------------------------

. estimates store boom2

. reg veryelect blackman whitewoman blackwoman if age>70, cluster(id)

Linear regression                               Number of obs     =        483
                                                F(3, 160)         =       1.24
                                                Prob > F          =     0.2986
                                                R-squared         =     0.0065
                                                Root MSE          =     .44917

                                   (Std. Err. adjusted for 161 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0336588   .0608613    -0.55   0.581    -.1538539    .0865363
  whitewoman |   -.033293   .0600615    -0.55   0.580    -.1519086    .0853226
  blackwoman |  -.1021303   .0561624    -1.82   0.071    -.2130454    .0087848
       _cons |   .3214286   .0451553     7.12   0.000     .2322512    .4106059
------------------------------------------------------------------------------

. estimates store silent2

. esttab genz2 mil2 genx2 boom2 silent2 using genveryelect, se rtf label addnot
> es("Standard errors clustered by subject") replace
(output written to genveryelect.rtf)

. 
. **IS INFERRED PARTISANSHIP DRIVING THE RESULTS OF STUDY I?**
. 
. //Now look at results from subjects in states with GOP or Democratic governor
> s.//
. //This helps us deal with inferred partisanship.//
. //If the subjects who see black and/or female candidates as unelectable are m
> ostly in GOP-governed//
. //states, then they might think those candidates are unelectable because they
>  are inferring//
. //that they're Democrats -- not because of their race/gender.//
. 
. //Appendix Table 1.18//
. reg electability blackman whitewoman blackwoman if gopgov==0, cluster(id)

Linear regression                               Number of obs     =      3,087
                                                F(3, 1028)        =       4.07
                                                Prob > F          =     0.0069
                                                R-squared         =     0.0039
                                                Root MSE          =     .84753

                                 (Std. Err. adjusted for 1,029 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0293966   .0434622     0.68   0.499    -.0558881    .1146813
  whitewoman |  -.0647965   .0423938    -1.53   0.127    -.1479847    .0183918
  blackwoman |  -.1065887   .0467386    -2.28   0.023    -.1983026   -.0148747
       _cons |   3.135309   .0320524    97.82   0.000     3.072414    3.198205
------------------------------------------------------------------------------

. estimates store demgov1

. reg veryelect blackman whitewoman blackwoman if gopgov==0, cluster(id)

Linear regression                               Number of obs     =      3,087
                                                F(3, 1028)        =       3.25
                                                Prob > F          =     0.0213
                                                R-squared         =     0.0031
                                                Root MSE          =     .47857

                                 (Std. Err. adjusted for 1,029 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0015818   .0252315     0.06   0.950    -.0479293    .0510928
  whitewoman |  -.0532448   .0242507    -2.20   0.028    -.1008314   -.0056582
  blackwoman |  -.0524448   .0250376    -2.09   0.036    -.1015753   -.0033142
       _cons |    .382732   .0181908    21.04   0.000     .3470367    .4184273
------------------------------------------------------------------------------

. estimates store demgov2

. reg electability blackman whitewoman blackwoman if gopgov==1, cluster(id)

Linear regression                               Number of obs     =      2,637
                                                F(3, 878)         =       5.04
                                                Prob > F          =     0.0018
                                                R-squared         =     0.0061
                                                Root MSE          =      .8443

                                   (Std. Err. adjusted for 879 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1168849   .0490522    -2.38   0.017    -.2131582   -.0206116
  whitewoman |  -.0584869    .046317    -1.26   0.207    -.1493918    .0324181
  blackwoman |  -.1777914   .0480143    -3.70   0.000    -.2720277   -.0835552
       _cons |   3.124438   .0332536    93.96   0.000     3.059172    3.189704
------------------------------------------------------------------------------

. estimates store gopgov1

. reg veryelect blackman whitewoman blackwoman if gopgov==1, cluster(id)

Linear regression                               Number of obs     =      2,637
                                                F(3, 878)         =       3.33
                                                Prob > F          =     0.0191
                                                R-squared         =     0.0039
                                                Root MSE          =     .46417

                                   (Std. Err. adjusted for 879 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0441441   .0271511    -1.63   0.104    -.0974327    .0091445
  whitewoman |  -.0347702   .0274212    -1.27   0.205     -.088589    .0190486
  blackwoman |  -.0809321   .0260845    -3.10   0.002    -.1321274   -.0297368
       _cons |   .3553223   .0201572    17.63   0.000     .3157605    .3948842
------------------------------------------------------------------------------

. estimates store gopgov2

. esttab demgov1 demgov2 gopgov1 gopgov2 using gov, se rtf label addnotes("Stan
> dard errors clustered by subject") replace
(output written to gov.rtf)

. 
. **HOW DO ESTIMATES OF OTHERS' UNWILLINGNESS TO VOTE FOR A FEMALE/BLACK PRESID
> ENT VARY BY SUBJECT DEMOGRAPHICS?**
. 
. //This section of code summarizes and analyzes each subject's estimates of th
> e percentage of other//
. //Americans who would not vote for a woman for president, and the percentage 
> of other Americans who//
. //would not vote for a black person for president.//
. 
. //Summary stats for "not vote for a woman" by respondent characteristics//
. //For APPENDIX TABLE 1.33//
. sum notvotewoman if age<35

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,797    45.18197    27.79759          0        100

. sum notvotewoman if 34<age & age<55

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      2,034    50.38348    27.22856          0        100

. sum notvotewoman if age>54

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,905    44.74331    23.17616          0        100

. 
. sum notvotewoman if femaleresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      2,979    47.63847    25.68394          0        100

. sum notvotewoman if maleresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      2,739    45.95728    26.77361          0        100

. 
. sum notvotewoman if education<3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,752    45.60959    27.02296          0        100

. //that's HS dropout or HS grad only//
. sum notvotewoman if education>2 & education<5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      2,235    45.50067    25.23768          0        100

. //that's some college but not a 4 yr degree//
. sum notvotewoman if education==5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,206    46.12438    25.61942          0        100

. //that's a 4 yr degree//
. sum notvotewoman if education==6

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        543    58.34254    26.65158          0        100

. //that's a postgrad degree//
. 
. sum notvotewoman if liberal==0 & conservative==0 & noideol==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,536    45.72461    25.48048          0        100

. sum notvotewoman if liberal==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,593    46.60075    25.88897          0        100

. sum notvotewoman if conserva==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,824    47.75987    27.12387          0        100

. 
. sum notvotewoman if dem==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      2,115     47.8156    26.41901          0        100

. sum notvotewoman if rep==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,803    48.62895    27.34367          0        100

. sum notvotewoman if indep==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      1,512    43.42857    24.19254          0        100

. 
. sum notvotewoman if whiteresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |      3,981    46.55162     25.7731          0        100

. sum notvotewoman if blackresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        681    49.24229    27.81084          0        100

. sum notvotewoman if hispanicresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        393    45.03053    29.06474          0        100

. sum notvotewoman if apiresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        276     48.8913     25.1963          0        100

. 
. //Summary stats for "not vote for a black person" by respondent characteristi
> cs//
. sum notvoteblack if age<35

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,797    41.14858    27.70853          0        100

. sum notvoteblack if 34<age & age<55

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,034    45.00885    28.71986          0        100

. sum notvoteblack if age>54

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,905    40.38583    23.91175          0        100

. 
. sum notvoteblack if femaleresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,979    41.43202     26.2189          0        100

. sum notvoteblack if maleresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,739    43.07558    27.64559          0        100

. 
. sum notvoteblack if education<3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,752    39.68836    27.68607          0        100

. **that's HS dropout or HS grad only**
. sum notvoteblack if education>2 & education<5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,235    41.14228    25.80315          0        100

. **that's some college but not a 4 yr degree**
. sum notvoteblack if education==5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,206    41.62935    25.85718          0        100

. **that's a 4 yr degree*
. sum notvoteblack if education==6

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        543    56.60221    27.40061          0        100

. **that's a postgrad degree**
. 
. sum notvoteblack if conserva==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,824     40.7023    27.61338          0        100

. sum notvoteblack if liberal==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,593    45.39925    27.01212          0        100

. sum notvoteblack if liberal==0 & conservative==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,319    41.33894    26.23135          0        100

. 
. sum notvoteblack if dem==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      2,115     44.3234    27.06804          0        100

. sum notvoteblack if rep==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,803    42.37105    28.19044          0        100

. sum notvoteblack if indep==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      1,512    39.24206    25.23354          0        100

. 
. sum notvoteblack if whiteresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |      3,981    41.32781     26.1481          0        100

. sum notvoteblack if blackresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        681    47.87665    29.76263          0        100

. sum notvoteblack if hispanicresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        393    40.09924     28.8769          0        100

. sum notvoteblack if apiresp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        276    42.48913    25.22098          0        100

. 
. **EVALUATING THE MECHANISM**
. **DO SUBJECTS' RESPONSES TO STUDY 1 VARY ACCORDING TO THEIR ESTIMATES OF OTHE
> RS' BIASES?**
. 
. //This section of code evaluations whether subjects who under- and over-estim
> ated others'//
. //biases responded differently to the experiment. The basic answer is: yes.//
. 
. //Because there are multiple ways to code who is an "over-estimator" and who 
> is an "under-estimator"//
. //I use a series of different cut points in the analysis below.//
. 
. //These tables look at differences between over- and under-estimators of othe
> rs' SEXISM//
. //The cut-points are 10%, 15%, 20%, 25%, and 30%//
. 
. //APPENDIX TABLE 1.19//
. reg elect blackman whitewoman blackwoman if notvotewoman<11, cluster(id)

Linear regression                               Number of obs     =        585
                                                F(3, 194)         =       0.86
                                                Prob > F          =     0.4625
                                                R-squared         =     0.0045
                                                Root MSE          =     .90305

                                   (Std. Err. adjusted for 195 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1413754   .1069682     1.32   0.188    -.0695945    .3523453
  whitewoman |     .05295   .0986225     0.54   0.592      -.14156      .24746
  blackwoman |  -.0152655   .1054524    -0.14   0.885    -.2232459    .1927149
       _cons |    2.96732   .0792874    37.42   0.000     2.810944    3.123696
------------------------------------------------------------------------------

. estimates store cut1

. reg veryelect blackman whitewoman blackwoman if notvotewoman<11, cluster(id) 
>  

Linear regression                               Number of obs     =        585
                                                F(3, 194)         =       0.90
                                                Prob > F          =     0.4404
                                                R-squared         =     0.0043
                                                Root MSE          =     .46695

                                   (Std. Err. adjusted for 195 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0558397   .0571297     0.98   0.330    -.0568353    .1685148
  whitewoman |  -.0299417   .0552761    -0.54   0.589    -.1389611    .0790776
  blackwoman |    .001343   .0539283     0.02   0.980    -.1050181    .1077041
       _cons |   .3137255   .0414463     7.57   0.000     .2319824    .3954686
------------------------------------------------------------------------------

. estimates store cut2

. reg elect blackman whitewoman blackwoman if notvotewoman>=11, cluster(id)

Linear regression                               Number of obs     =      5,151
                                                F(3, 1716)        =       6.54
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0043
                                                Root MSE          =     .83926

                                 (Std. Err. adjusted for 1,717 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0600699   .0342353    -1.75   0.080    -.1272171    .0070773
  whitewoman |  -.0753306   .0328971    -2.29   0.022    -.1398533    -.010808
  blackwoman |  -.1546139   .0352995    -4.38   0.000    -.2238486   -.0853792
       _cons |   3.149923    .023967   131.43   0.000     3.102915     3.19693
------------------------------------------------------------------------------

. estimates store cut3

. reg veryelect blackman whitewoman blackwoman if notvotewoman>=11, cluster(id)

Linear regression                               Number of obs     =      5,151
                                                F(3, 1716)        =       5.28
                                                Prob > F          =     0.0013
                                                R-squared         =     0.0032
                                                Root MSE          =     .47291

                                 (Std. Err. adjusted for 1,717 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |    -.02856   .0195229    -1.46   0.144    -.0668513    .0097313
  whitewoman |  -.0461271   .0192122    -2.40   0.016    -.0838089   -.0084453
  blackwoman |  -.0737723   .0191473    -3.85   0.000    -.1113268   -.0362177
       _cons |   .3763524   .0142427    26.42   0.000     .3484174    .4042874
------------------------------------------------------------------------------

. estimates store cut4

. esttab cut1 cut2 cut3 cut4 using nvw10, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to nvw10.rtf)

. 
. //APPENDIX TABLE 1.20//
. reg elect blackman whitewoman blackwoman if notvotewoman<16, cluster(id)

Linear regression                               Number of obs     =        759
                                                F(3, 252)         =       0.84
                                                Prob > F          =     0.4712
                                                R-squared         =     0.0031
                                                Root MSE          =     .87914

                                   (Std. Err. adjusted for 253 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1197917   .0913196     1.31   0.191    -.0600552    .2996385
  whitewoman |   .1057653   .0831882     1.27   0.205    -.0580675     .269598
  blackwoman |   .0364583   .0892023     0.41   0.683    -.1392187    .2121353
       _cons |   2.963542   .0680589    43.54   0.000     2.829505    3.097578
------------------------------------------------------------------------------

. estimates store cut5

. reg veryelect blackman whitewoman blackwoman if notvotewoman<16, cluster(id) 
>  

Linear regression                               Number of obs     =        759
                                                F(3, 252)         =       0.87
                                                Prob > F          =     0.4548
                                                R-squared         =     0.0034
                                                Root MSE          =     .46594

                                   (Std. Err. adjusted for 253 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0694444   .0498729     1.39   0.165    -.0287764    .1676653
  whitewoman |    .005363   .0482079     0.11   0.912    -.0895786    .1003047
  blackwoman |   .0326577    .046592     0.70   0.484    -.0591018    .1244171
       _cons |   .2916667   .0359554     8.11   0.000     .2208553    .3624781
------------------------------------------------------------------------------

. estimates store cut6

. reg elect blackman whitewoman blackwoman if notvotewoman>=16, cluster(id)

Linear regression                               Number of obs     =      4,977
                                                F(3, 1658)        =       7.36
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0050
                                                Root MSE          =     .84068

                                 (Std. Err. adjusted for 1,659 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   -.063301   .0348966    -1.81   0.070     -.131747     .005145
  whitewoman |  -.0872377   .0336465    -2.59   0.010    -.1532319   -.0212435
  blackwoman |  -.1666592   .0360527    -4.62   0.000    -.2373728   -.0959455
       _cons |   3.156175     .02436   129.56   0.000     3.108396    3.203955
------------------------------------------------------------------------------

. estimates store cut7

. reg veryelect blackman whitewoman blackwoman if notvotewoman>=16, cluster(id)

Linear regression                               Number of obs     =      4,977
                                                F(3, 1658)        =       6.06
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0038
                                                Root MSE          =     .47311

                                 (Std. Err. adjusted for 1,659 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0333947   .0198574    -1.68   0.093     -.072343    .0055536
  whitewoman |  -.0515841   .0195569    -2.64   0.008    -.0899429   -.0132252
  blackwoman |  -.0808669   .0195181    -4.14   0.000    -.1191496   -.0425841
       _cons |   .3816733   .0144848    26.35   0.000     .3532628    .4100838
------------------------------------------------------------------------------

. estimates store cut8

. esttab cut5 cut6 cut7 cut8 using nvw15, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to nvw15.rtf)

. 
. //APPENDIX TABLE 1.21//
. reg elect blackman whitewoman blackwoman if notvotewoman<21, cluster(id)

Linear regression                               Number of obs     =      1,041
                                                F(3, 346)         =       0.59
                                                Prob > F          =     0.6217
                                                R-squared         =     0.0016
                                                Root MSE          =     .85772

                                   (Std. Err. adjusted for 347 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0751409    .075959     0.99   0.323    -.0742587    .2245404
  whitewoman |   .0850934   .0707789     1.20   0.230    -.0541176    .2243045
  blackwoman |   .0296317   .0745132     0.40   0.691    -.1169241    .1761875
       _cons |    3.02682    .056811    53.28   0.000     2.915082    3.138558
------------------------------------------------------------------------------

. estimates store cut9

. reg veryelect blackman whitewoman blackwoman if notvotewoman<21, cluster(id) 
>  

Linear regression                               Number of obs     =      1,041
                                                F(3, 346)         =       0.15
                                                Prob > F          =     0.9289
                                                R-squared         =     0.0004
                                                Root MSE          =     .47322

                                   (Std. Err. adjusted for 347 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0233491   .0427745     0.55   0.586    -.0607816    .1074798
  whitewoman |   .0028494   .0418649     0.07   0.946    -.0794924    .0851911
  blackwoman |   .0170714   .0407671     0.42   0.676    -.0631111     .097254
       _cons |   .3256705   .0310241    10.50   0.000      .264651      .38669
------------------------------------------------------------------------------

. estimates store cut10

. reg elect blackman whitewoman blackwoman if notvotewoman>=21, cluster(id)

Linear regression                               Number of obs     =      4,695
                                                F(3, 1564)        =       7.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0057
                                                Root MSE          =     .84331

                                 (Std. Err. adjusted for 1,565 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0640192   .0360953    -1.77   0.076    -.1348195    .0067811
  whitewoman |  -.0947351   .0346993    -2.73   0.006    -.1627972    -.026673
  blackwoman |  -.1763967   .0372979    -4.73   0.000    -.2495558   -.1032375
       _cons |   3.153457   .0251018   125.63   0.000      3.10422    3.202694
------------------------------------------------------------------------------

. estimates store cut11

. reg veryelect blackman whitewoman blackwoman if notvotewoman>=21, cluster(id)

Linear regression                               Number of obs     =      4,695
                                                F(3, 1564)        =       6.46
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0043
                                                Root MSE          =     .47207

                                 (Std. Err. adjusted for 1,565 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0293415   .0204725    -1.43   0.152    -.0694978    .0108149
  whitewoman |  -.0547289   .0200918    -2.72   0.007    -.0941386   -.0153192
  blackwoman |  -.0837597   .0200773    -4.17   0.000     -.123141   -.0443784
       _cons |   .3794266   .0149247    25.42   0.000     .3501522    .4087011
------------------------------------------------------------------------------

. estimates store cut12

. esttab cut9 cut10 cut11 cut12 using nvw20, se rtf label addnotes("Standard er
> rors clustered by subject") replace
(output written to nvw20.rtf)

. 
. //APPENDIX TABLE 1.22//
. reg elect blackman whitewoman blackwoman if notvotewoman<26, cluster(id)

Linear regression                               Number of obs     =      1,254
                                                F(3, 417)         =       0.47
                                                Prob > F          =     0.7063
                                                R-squared         =     0.0011
                                                Root MSE          =     .84984

                                   (Std. Err. adjusted for 418 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0620942   .0686566     0.90   0.366     -.072862    .1970505
  whitewoman |   .0536411    .065684     0.82   0.415    -.0754719    .1827541
  blackwoman |   .0026203   .0691269     0.04   0.970    -.1332604    .1385009
       _cons |   3.045161   .0522371    58.30   0.000      2.94248    3.147842
------------------------------------------------------------------------------

. estimates store cut13

. reg veryelect blackman whitewoman blackwoman if notvotewoman<26, cluster(id) 
>  

Linear regression                               Number of obs     =      1,254
                                                F(3, 417)         =       0.19
                                                Prob > F          =     0.9013
                                                R-squared         =     0.0005
                                                Root MSE          =     .47288

                                   (Std. Err. adjusted for 418 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0210542   .0381858     0.55   0.582    -.0540064    .0961148
  whitewoman |  -.0029167    .038467    -0.08   0.940    -.0785301    .0726967
  blackwoman |   -.004613   .0379304    -0.12   0.903    -.0791716    .0699456
       _cons |   .3322581   .0285751    11.63   0.000      .276089    .3884272
------------------------------------------------------------------------------

. estimates store cut14

. reg elect blackman whitewoman blackwoman if notvotewoman>=26, cluster(id)

Linear regression                               Number of obs     =      4,482
                                                F(3, 1493)        =       7.53
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0057
                                                Root MSE          =     .84495

                                 (Std. Err. adjusted for 1,494 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0666836    .037068    -1.80   0.072    -.1393945    .0060273
  whitewoman |  -.0939683   .0354336    -2.65   0.008    -.1634732   -.0244634
  blackwoman |  -.1777654   .0380761    -4.67   0.000    -.2524537   -.1030771
       _cons |   3.153914   .0255583   123.40   0.000      3.10378    3.204048
------------------------------------------------------------------------------

. estimates store cut15

. reg veryelect blackman whitewoman blackwoman if notvotewoman>=26, cluster(id)

Linear regression                               Number of obs     =      4,482
                                                F(3, 1493)        =       5.98
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0042
                                                Root MSE          =     .47218

                                 (Std. Err. adjusted for 1,494 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0310264   .0210877    -1.47   0.141    -.0723911    .0103384
  whitewoman |  -.0556965   .0205357    -2.71   0.007    -.0959784   -.0154147
  blackwoman |   -.082244   .0204863    -4.01   0.000     -.122429   -.0420591
       _cons |   .3799472   .0152495    24.92   0.000     .3500344      .40986
------------------------------------------------------------------------------

. estimates store cut16

. esttab cut13 cut14 cut15 cut16 using nvw25, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvw25.rtf)

. 
. //APPENDIX TABLE 1.23//
. reg elect blackman whitewoman blackwoman if notvotewoman<31, cluster(id)

Linear regression                               Number of obs     =      1,701
                                                F(3, 566)         =       0.50
                                                Prob > F          =     0.6830
                                                R-squared         =     0.0009
                                                Root MSE          =     .84263

                                   (Std. Err. adjusted for 567 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0410628    .057908     0.71   0.479     -.072678    .1548036
  whitewoman |   .0016527   .0563585     0.03   0.977    -.1090447    .1123501
  blackwoman |  -.0293331   .0581063    -0.50   0.614    -.1434634    .0847973
       _cons |   3.077295   .0426461    72.16   0.000     2.993531    3.161059
------------------------------------------------------------------------------

. estimates store cut17

. reg veryelect blackman whitewoman blackwoman if notvotewoman<31, cluster(id) 
>  

Linear regression                               Number of obs     =      1,701
                                                F(3, 566)         =       0.86
                                                Prob > F          =     0.4627
                                                R-squared         =     0.0015
                                                Root MSE          =     .47242

                                   (Std. Err. adjusted for 567 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0434783   .0329994     1.32   0.188     -.021338    .1082945
  whitewoman |  -.0034961   .0326289    -0.11   0.915    -.0675847    .0605926
  blackwoman |   .0096618   .0323547     0.30   0.765     -.053888    .0732117
       _cons |   .3236715   .0243279    13.30   0.000     .2758875    .3714554
------------------------------------------------------------------------------

. estimates store cut18

. reg elect blackman whitewoman blackwoman if notvotewoman>=31, cluster(id)

Linear regression                               Number of obs     =      4,035
                                                F(3, 1344)        =       7.05
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0061
                                                Root MSE          =     .84751

                                 (Std. Err. adjusted for 1,345 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0711963   .0393797    -1.81   0.071    -.1484487     .006056
  whitewoman |  -.0876331   .0374424    -2.34   0.019     -.161085   -.0141812
  blackwoman |  -.1847226   .0406301    -4.55   0.000     -.264428   -.1050172
       _cons |   3.151985   .0273179   115.38   0.000     3.098394    3.205575
------------------------------------------------------------------------------

. estimates store cut19

. reg veryelect blackman whitewoman blackwoman if notvotewoman>=31, cluster(id)

Linear regression                               Number of obs     =      4,035
                                                F(3, 1344)        =       6.86
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0054
                                                Root MSE          =     .47199

                                 (Std. Err. adjusted for 1,345 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0453326   .0222056    -2.04   0.041    -.0888939   -.0017713
  whitewoman |  -.0603041   .0217554    -2.77   0.006    -.1029824   -.0176259
  blackwoman |  -.0965231   .0216325    -4.46   0.000    -.1389603   -.0540859
       _cons |   .3881897   .0160851    24.13   0.000     .3566351    .4197444
------------------------------------------------------------------------------

. estimates store cut20

. esttab cut17 cut18 cut19 cut20 using nvw30, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvw30.rtf)

. 
. //Now we'll repeat this entire exercise for the over- and under-estimators of
>  others' racism.//
. 
. //Actually, I'll start the cut-points at 5%, because there are more subjects 
> with very low//
. //estimates of others' racism.//
. 
. //APPENDIX TABLE 1.24//
. reg elect blackman whitewoman blackwoman if notvoteblack<6, cluster(id)

Linear regression                               Number of obs     =        525
                                                F(3, 174)         =       2.13
                                                Prob > F          =     0.0977
                                                R-squared         =     0.0122
                                                Root MSE          =        .91

                                   (Std. Err. adjusted for 175 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .2810334    .115165     2.44   0.016     .0537331    .5083336
  whitewoman |   .0719424   .1039903     0.69   0.490    -.1333023    .2771872
  blackwoman |   .0571276   .1135249     0.50   0.615    -.1669356    .2811908
       _cons |   2.928058   .0800793    36.56   0.000     2.770006    3.086109
------------------------------------------------------------------------------

. estimates store cut24

. reg veryelect blackman whitewoman blackwoman if notvoteblack<6, cluster(id)  

Linear regression                               Number of obs     =        525
                                                F(3, 174)         =       2.75
                                                Prob > F          =     0.0444
                                                R-squared         =     0.0160
                                                Root MSE          =     .46737

                                   (Std. Err. adjusted for 175 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1395029   .0629977     2.21   0.028     .0151649     .263841
  whitewoman |  -.0182662   .0564604    -0.32   0.747    -.1297017    .0931692
  blackwoman |   .0603784   .0588109     1.03   0.306    -.0556963     .176453
       _cons |   .2877698   .0410321     7.01   0.000     .2067851    .3687544
------------------------------------------------------------------------------

. estimates store cut25

. reg elect blackman whitewoman blackwoman if notvoteblack>=6, cluster(id)

Linear regression                               Number of obs     =      5,211
                                                F(3, 1736)        =       7.08
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0046
                                                Root MSE          =     .83887

                                 (Std. Err. adjusted for 1,737 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0702605   .0339549    -2.07   0.039    -.1368572   -.0036637
  whitewoman |  -.0756337    .032667    -2.32   0.021    -.1397045   -.0115629
  blackwoman |  -.1606678   .0349995    -4.59   0.000    -.2293133   -.0920223
       _cons |   3.152141   .0239555   131.58   0.000     3.105156    3.199125
------------------------------------------------------------------------------

. estimates store cut26

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=6, cluster(id)

Linear regression                               Number of obs     =      5,211
                                                F(3, 1736)        =       6.01
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0036
                                                Root MSE          =     .47247

                                 (Std. Err. adjusted for 1,737 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0349984   .0193336    -1.81   0.070    -.0729179    .0029212
  whitewoman |  -.0469102    .019132    -2.45   0.014    -.0844344    -.009386
  blackwoman |  -.0792156   .0189549    -4.18   0.000    -.1163925   -.0420387
       _cons |   .3784404   .0142329    26.59   0.000     .3505249    .4063559
------------------------------------------------------------------------------

. estimates store cut27

. esttab cut24 cut25 cut26 cut27 using nvb5, se rtf label addnotes("Standard er
> rors clustered by subject") replace
(output written to nvb5.rtf)

. 
. //APPENDIX TABLE 1.25//
. reg elect blackman whitewoman blackwoman if notvoteblack<11, cluster(id)

Linear regression                               Number of obs     =        822
                                                F(3, 273)         =       2.38
                                                Prob > F          =     0.0702
                                                R-squared         =     0.0089
                                                Root MSE          =     .89348

                                   (Std. Err. adjusted for 274 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1656762   .0911879     1.82   0.070    -.0138446     .345197
  whitewoman |    .008613   .0804247     0.11   0.915    -.1497183    .1669444
  blackwoman |  -.0733313   .0923298    -0.79   0.428    -.2551003    .1084376
       _cons |   3.014218   .0639316    47.15   0.000     2.888356     3.14008
------------------------------------------------------------------------------

. estimates store cut20

. reg veryelect blackman whitewoman blackwoman if notvoteblack<11, cluster(id) 
>  

Linear regression                               Number of obs     =        822
                                                F(3, 273)         =       2.69
                                                Prob > F          =     0.0464
                                                R-squared         =     0.0103
                                                Root MSE          =     .46882

                                   (Std. Err. adjusted for 274 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0856842   .0502634     1.70   0.089     -.013269    .1846373
  whitewoman |   -.039343   .0464981    -0.85   0.398    -.1308834    .0521975
  blackwoman |  -.0265216   .0470127    -0.56   0.573    -.1190751    .0660319
       _cons |   .3270142   .0353072     9.26   0.000     .2575053    .3965231
------------------------------------------------------------------------------

. estimates store cut21

. reg elect blackman whitewoman blackwoman if notvoteblack>=11, cluster(id)

Linear regression                               Number of obs     =      4,914
                                                F(3, 1637)        =       5.94
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0041
                                                Root MSE          =     .83765

                                 (Std. Err. adjusted for 1,638 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0722596   .0348798    -2.07   0.038    -.1406733    -.003846
  whitewoman |  -.0731828   .0338382    -2.16   0.031    -.1395536    -.006812
  blackwoman |  -.1513038   .0358796    -4.22   0.000    -.2216786   -.0809289
       _cons |   3.150485   .0246262   127.93   0.000     3.102183    3.198788
------------------------------------------------------------------------------

. estimates store cut22

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=11, cluster(id)

Linear regression                               Number of obs     =      4,914
                                                F(3, 1637)        =       4.66
                                                Prob > F          =     0.0030
                                                R-squared         =     0.0030
                                                Root MSE          =     .47272

                                 (Std. Err. adjusted for 1,638 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0367001   .0198634    -1.85   0.065    -.0756604    .0022602
  whitewoman |  -.0447858    .019702    -2.27   0.023    -.0834296    -.006142
  blackwoman |  -.0726037   .0195538    -3.71   0.000    -.1109568   -.0342505
       _cons |   .3770227   .0145738    25.87   0.000     .3484375    .4056078
------------------------------------------------------------------------------

. estimates store cut23

. esttab cut20 cut21 cut22 cut23 using nvb10, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvb10.rtf)

. 
. //APPENDIX TABLE 1.26//
. reg elect blackman whitewoman blackwoman if notvoteblack<16, cluster(id)

Linear regression                               Number of obs     =      1,053
                                                F(3, 350)         =       2.44
                                                Prob > F          =     0.0642
                                                R-squared         =     0.0072
                                                Root MSE          =     .87292

                                   (Std. Err. adjusted for 351 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1269228   .0785469     1.62   0.107    -.0275606    .2814062
  whitewoman |  -.0196853   .0703469    -0.28   0.780    -.1580411    .1186705
  blackwoman |  -.0797206   .0778441    -1.02   0.306    -.2328216    .0733805
       _cons |   3.037736   .0554906    54.74   0.000     2.928599    3.146873
------------------------------------------------------------------------------

. estimates store cut30

. reg veryelect blackman whitewoman blackwoman if notvoteblack<16, cluster(id) 
>  

Linear regression                               Number of obs     =      1,053
                                                F(3, 350)         =       4.61
                                                Prob > F          =     0.0035
                                                R-squared         =     0.0143
                                                Root MSE          =     .46476

                                   (Std. Err. adjusted for 351 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1006896   .0438232     2.30   0.022     .0144996    .1868795
  whitewoman |   -.046223   .0407828    -1.13   0.258    -.1264333    .0339873
  blackwoman |  -.0269048   .0401442    -0.67   0.503     -.105859    .0520494
       _cons |   .3169811   .0308952    10.26   0.000     .2562176    .3777446
------------------------------------------------------------------------------

. estimates store cut31

. reg elect blackman whitewoman blackwoman if notvoteblack>=16, cluster(id)

Linear regression                               Number of obs     =      4,683
                                                F(3, 1560)        =       5.71
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0042
                                                Root MSE          =     .83973

                                 (Std. Err. adjusted for 1,561 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0751671   .0358075    -2.10   0.036     -.145403   -.0049311
  whitewoman |  -.0702638   .0347856    -2.02   0.044    -.1384953   -.0020323
  blackwoman |  -.1531579   .0370579    -4.13   0.000    -.2258465   -.0804693
       _cons |   3.151438   .0252608   124.76   0.000     3.101889    3.200987
------------------------------------------------------------------------------

. estimates store cut32

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=16, cluster(id)

Linear regression                               Number of obs     =      4,683
                                                F(3, 1560)        =       4.58
                                                Prob > F          =     0.0034
                                                R-squared         =     0.0032
                                                Root MSE          =     .47343

                                 (Std. Err. adjusted for 1,561 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0459635   .0203197    -2.26   0.024    -.0858204   -.0061066
  whitewoman |   -.043042   .0202307    -2.13   0.034    -.0827242   -.0033598
  blackwoman |  -.0745919   .0201766    -3.70   0.000     -.114168   -.0350159
       _cons |   .3815567   .0149478    25.53   0.000     .3522369    .4108765
------------------------------------------------------------------------------

. estimates store cut33

. esttab cut30 cut31 cut32 cut33 using nvb15, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvb15.rtf)

. 
. //APPENDIX TABLE 1.27//
. reg elect blackman whitewoman blackwoman if notvoteblack<21, cluster(id)

Linear regression                               Number of obs     =      1,383
                                                F(3, 460)         =       2.71
                                                Prob > F          =     0.0446
                                                R-squared         =     0.0059
                                                Root MSE          =      .8588

                                   (Std. Err. adjusted for 461 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1399548   .0663523     2.11   0.035     .0095636    .2703459
  whitewoman |   .0176471   .0613874     0.29   0.774    -.1029874    .1382815
  blackwoman |  -.0407328    .065668    -0.62   0.535    -.1697794    .0883137
       _cons |   3.032353   .0473385    64.06   0.000     2.939326    3.125379
------------------------------------------------------------------------------

. estimates store cut40

. reg veryelect blackman whitewoman blackwoman if notvoteblack<21, cluster(id) 
>  

Linear regression                               Number of obs     =      1,383
                                                F(3, 460)         =       4.14
                                                Prob > F          =     0.0065
                                                R-squared         =     0.0099
                                                Root MSE          =     .46794

                                   (Std. Err. adjusted for 461 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .1123077   .0375099     2.99   0.003     .0385958    .1860196
  whitewoman |  -1.58e-15   .0359252    -0.00   1.000    -.0705978    .0705978
  blackwoman |   .0072626   .0348854     0.21   0.835     -.061292    .0758172
       _cons |         .3   .0263606    11.38   0.000     .2481979    .3518021
------------------------------------------------------------------------------

. estimates store cut41

. reg elect blackman whitewoman blackwoman if notvoteblack>=21, cluster(id)

Linear regression                               Number of obs     =      4,353
                                                F(3, 1450)        =       6.42
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0051
                                                Root MSE          =     .84156

                                 (Std. Err. adjusted for 1,451 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0928602   .0373431    -2.49   0.013    -.1661124   -.0196079
  whitewoman |  -.0854461     .03617    -2.36   0.018    -.1563973   -.0144949
  blackwoman |   -.170167   .0388202    -4.38   0.000    -.2463168   -.0940172
       _cons |   3.160795   .0262935   120.21   0.000     3.109218    3.212372
------------------------------------------------------------------------------

. estimates store cut42

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=21, cluster(id)

Linear regression                               Number of obs     =      4,353
                                                F(3, 1450)        =       6.06
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0046
                                                Root MSE          =     .47291

                                 (Std. Err. adjusted for 1,451 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0596255   .0211122    -2.82   0.005    -.1010393   -.0182117
  whitewoman |  -.0571938   .0209669    -2.73   0.006    -.0983225    -.016065
  blackwoman |  -.0884293   .0210031    -4.21   0.000    -.1296291   -.0472296
       _cons |   .3911472     .01556    25.14   0.000     .3606248    .4216697
------------------------------------------------------------------------------

. estimates store cut43

. esttab cut40 cut41 cut42 cut43 using nvb20, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvb20.rtf)

. 
. //APPENDIX TABLE 1.28//
. reg elect blackman whitewoman blackwoman if notvoteblack<26, cluster(id)

Linear regression                               Number of obs     =      1,704
                                                F(3, 567)         =       1.06
                                                Prob > F          =     0.3672
                                                R-squared         =     0.0019
                                                Root MSE          =      .8523

                                   (Std. Err. adjusted for 568 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0662727   .0579027     1.14   0.253    -.0474572    .1800026
  whitewoman |    .020015    .055857     0.36   0.720    -.0896969    .1297268
  blackwoman |  -.0364643   .0588817    -0.62   0.536    -.1521172    .0791886
       _cons |   3.055156   .0421867    72.42   0.000     2.972295    3.138017
------------------------------------------------------------------------------

. estimates store cut50

. reg veryelect blackman whitewoman blackwoman if notvoteblack<26, cluster(id) 
>  

Linear regression                               Number of obs     =      1,704
                                                F(3, 567)         =       1.68
                                                Prob > F          =     0.1696
                                                R-squared         =     0.0031
                                                Root MSE          =     .47017

                                   (Std. Err. adjusted for 568 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0644399   .0328745     1.96   0.050    -.0001308    .1290105
  whitewoman |    .007156   .0329851     0.22   0.828     -.057632     .071944
  blackwoman |     .00367    .031996     0.11   0.909    -.0591751    .0665151
       _cons |   .3117506   .0240049    12.99   0.000     .2646012       .3589
------------------------------------------------------------------------------

. estimates store cut51

. reg elect blackman whitewoman blackwoman if notvoteblack>=26, cluster(id)

Linear regression                               Number of obs     =      4,032
                                                F(3, 1343)        =       6.82
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0059
                                                Root MSE          =     .84338

                                 (Std. Err. adjusted for 1,344 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0818786   .0393618    -2.08   0.038    -.1590959   -.0046613
  whitewoman |     -.0949   .0375264    -2.53   0.012    -.1685166   -.0212833
  blackwoman |  -.1822282   .0405189    -4.50   0.000    -.2617155    -.102741
       _cons |   3.161165   .0274203   115.29   0.000     3.107374    3.214956
------------------------------------------------------------------------------

. estimates store cut52

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=26, cluster(id)

Linear regression                               Number of obs     =      4,032
                                                F(3, 1343)        =       6.50
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0052
                                                Root MSE          =      .4728

                                 (Std. Err. adjusted for 1,344 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0542544    .022214    -2.44   0.015    -.0978323   -.0106765
  whitewoman |  -.0648906   .0216388    -3.00   0.003    -.1073401   -.0224412
  blackwoman |  -.0943072    .021758    -4.33   0.000    -.1369906   -.0516238
       _cons |   .3932039   .0161519    24.34   0.000     .3615183    .4248895
------------------------------------------------------------------------------

. estimates store cut53

. esttab cut50 cut51 cut52 cut53 using nvb25, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvb25.rtf)

. 
. //APPENDIX TABLE 1.29//
. reg elect blackman whitewoman blackwoman if notvoteblack<31, cluster(id)

Linear regression                               Number of obs     =      2,166
                                                F(3, 721)         =       1.04
                                                Prob > F          =     0.3737
                                                R-squared         =     0.0015
                                                Root MSE          =     .84923

                                   (Std. Err. adjusted for 722 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0254614   .0518996     0.49   0.624    -.0764311    .1273539
  whitewoman |  -.0148592   .0506303    -0.29   0.769    -.1142596    .0845413
  blackwoman |  -.0652399   .0525438    -1.24   0.215     -.168397    .0379173
       _cons |       3.08   .0377181    81.66   0.000      3.00595     3.15405
------------------------------------------------------------------------------

. estimates store cut60

. reg veryelect blackman whitewoman blackwoman if notvoteblack<31, cluster(id) 
>  

Linear regression                               Number of obs     =      2,166
                                                F(3, 721)         =       1.15
                                                Prob > F          =     0.3290
                                                R-squared         =     0.0017
                                                Root MSE          =     .47062

                                   (Std. Err. adjusted for 722 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0282486   .0297267     0.95   0.342    -.0301126    .0866097
  whitewoman |  -.0129108   .0299938    -0.43   0.667    -.0717964    .0459748
  blackwoman |  -.0233702   .0287721    -0.81   0.417    -.0798573    .0331168
       _cons |   .3333333   .0218405    15.26   0.000     .2904548    .3762119
------------------------------------------------------------------------------

. estimates store cut61

. reg elect blackman whitewoman blackwoman if notvoteblack>=31, cluster(id)

Linear regression                               Number of obs     =      3,570
                                                F(3, 1189)        =       6.10
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0060
                                                Root MSE          =     .84436

                                 (Std. Err. adjusted for 1,190 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0759171   .0418435    -1.81   0.070    -.1580123    .0061782
  whitewoman |  -.0879251   .0395253    -2.22   0.026    -.1654722   -.0103779
  blackwoman |  -.1832186   .0432057    -4.24   0.000    -.2679865   -.0984506
       _cons |   3.159436   .0290081   108.92   0.000     3.102523    3.216349
------------------------------------------------------------------------------

. estimates store cut62

. reg veryelect blackman whitewoman blackwoman if notvoteblack>=31, cluster(id)

Linear regression                               Number of obs     =      3,570
                                                F(3, 1189)        =       5.38
                                                Prob > F          =     0.0011
                                                R-squared         =     0.0048
                                                Root MSE          =     .47323

                                 (Std. Err. adjusted for 1,190 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0474711   .0235252    -2.02   0.044    -.0936267   -.0013156
  whitewoman |  -.0617358   .0226956    -2.72   0.007    -.1062636    -.017208
  blackwoman |  -.0903423   .0230915    -3.91   0.000     -.135647   -.0450376
       _cons |   .3904555   .0170471    22.90   0.000     .3570099    .4239012
------------------------------------------------------------------------------

. estimates store cut63

. esttab cut60 cut61 cut62 cut63 using nvb30, se rtf label addnotes("Standard e
> rrors clustered by subject") replace
(output written to nvb30.rtf)

.  
. **DO SUBJECTS FROM STATES THAT HAVE HAD DIVERSE GOVERNORS SEE**
. **FEMALE AND BLACK CANDIDATES AS MORE ELECTABLE?**
. 
. //In short, not really.//
. 
. //Let's look at subjects from states that have had (mostly white) female gove
> rnors or black (male) governors//
. //Do people from those states think differently about who is "electable"?//
. 
. //(Note that the US has never had a black female governor)//
. 
. //APPENDIX TABLE 1.31//
. reg elect blackman whitewoman blackwoman if femalegovnow==1, cluster(id)

Linear regression                               Number of obs     =        495
                                                F(3, 164)         =       0.89
                                                Prob > F          =     0.4456
                                                R-squared         =     0.0048
                                                Root MSE          =     .86005

                                   (Std. Err. adjusted for 165 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |   .0158432   .1123321     0.14   0.888    -.2059603    .2376468
  whitewoman |   .0747863   .1166489     0.64   0.522     -.155541    .3051137
  blackwoman |  -.0898478   .1124422    -0.80   0.425    -.3118689    .1321733
       _cons |   3.008547   .0889342    33.83   0.000     2.832943    3.184151
------------------------------------------------------------------------------

. estimates store fgovnow1

. reg veryelect blackman whitewoman blackwoman if femalegovnow==1, cluster(id)

Linear regression                               Number of obs     =        495
                                                F(3, 164)         =       2.03
                                                Prob > F          =     0.1119
                                                R-squared         =     0.0103
                                                Root MSE          =      .4599

                                   (Std. Err. adjusted for 165 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0414843   .0604704    -0.69   0.494    -.1608852    .0779167
  whitewoman |   -.017094   .0641851    -0.27   0.790    -.1438297    .1096416
  blackwoman |  -.1227851   .0590751    -2.08   0.039    -.2394308   -.0061393
       _cons |   .3504274   .0461656     7.59   0.000     .2592717     .441583
------------------------------------------------------------------------------

. estimates store fgovnow2

. esttab fgovnow1 fgovnow2 using fgovnow, se rtf label addnotes("Standard error
> s clustered by subject") replace
(output written to fgovnow.rtf)

. 
. //APPENDIX TABLE 1.30//
. reg elect blackman whitewoman blackwoman if femalegov30==1, cluster(id)

Linear regression                               Number of obs     =      2,337
                                                F(3, 778)         =       2.71
                                                Prob > F          =     0.0439
                                                R-squared         =     0.0038
                                                Root MSE          =     .84327

                                   (Std. Err. adjusted for 779 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0341571   .0515876    -0.66   0.508    -.1354244    .0671102
  whitewoman |  -.0352229   .0504894    -0.70   0.486    -.1343345    .0638886
  blackwoman |  -.1398793   .0527653    -2.65   0.008    -.2434586      -.0363
       _cons |   3.096552   .0366104    84.58   0.000     3.024685    3.168419
------------------------------------------------------------------------------

. estimates store fgov301

. reg veryelect blackman whitewoman blackwoman if femalegov30==1, cluster(id)

Linear regression                               Number of obs     =      2,337
                                                F(3, 778)         =       2.22
                                                Prob > F          =     0.0840
                                                R-squared         =     0.0027
                                                Root MSE          =     .46643

                                   (Std. Err. adjusted for 779 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0143804   .0286181    -0.50   0.615    -.0705583    .0417974
  whitewoman |  -.0314104   .0289082    -1.09   0.278    -.0881578    .0253369
  blackwoman |  -.0657802   .0275328    -2.39   0.017    -.1198276   -.0117328
       _cons |   .3482759   .0209253    16.64   0.000      .307199    .3893527
------------------------------------------------------------------------------

. estimates store fgov302

. esttab fgov301 fgov302 using fgov30, se rtf label addnotes("Standard errors c
> lustered by subject") replace
(output written to fgov30.rtf)

. 
. //APPENDIX TABLE 1.32//
. reg elect blackman whitewoman blackwoman if blackgov30==1, cluster(id)

Linear regression                               Number of obs     =        681
                                                F(3, 226)         =       1.36
                                                Prob > F          =     0.2573
                                                R-squared         =     0.0067
                                                Root MSE          =     .82283

                                   (Std. Err. adjusted for 227 clusters in id)
------------------------------------------------------------------------------
             |               Robust
electability |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.1569594   .0928375    -1.69   0.092    -.3398972    .0259784
  whitewoman |  -.1177831   .0851507    -1.38   0.168    -.2855739    .0500077
  blackwoman |  -.1707665   .0957691    -1.78   0.076    -.3594811    .0179481
       _cons |   3.315152   .0653736    50.71   0.000     3.186332    3.443971
------------------------------------------------------------------------------

. estimates store bgov301

. reg veryelect blackman whitewoman blackwoman if blackgov30==1, cluster(id)

Linear regression                               Number of obs     =        681
                                                F(3, 226)         =       1.39
                                                Prob > F          =     0.2459
                                                R-squared         =     0.0065
                                                Root MSE          =     .49266

                                   (Std. Err. adjusted for 227 clusters in id)
------------------------------------------------------------------------------
             |               Robust
   veryelect |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    blackman |  -.0893683   .0572724    -1.56   0.120    -.2022244    .0234879
  whitewoman |  -.1032695   .0533439    -1.94   0.054    -.2083845    .0018454
  blackwoman |   -.083779   .0567224    -1.48   0.141    -.1955514    .0279935
       _cons |   .4848485   .0429014    11.30   0.000     .4003106    .5693864
------------------------------------------------------------------------------

. estimates store bgov302

. esttab bgov301 bgov302 using bgov30, se rtf label addnotes("Standard errors c
> lustered by subject") replace
(output written to bgov30.rtf)

. 
. //Whew! That's the end of this do-file.//
. 
. //To replicate the Study 2 results, please use the do-file Study2_Analysis.do
> //
. 
. clear 

. 
end of do-file

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the Study 2 results//
. 
. //First, the do-file cleans and re-organizes the dataset.//
. //Then, the "Analysis 1" section provides the main results in the manuscript.
> //
. //Last, the "Analysis 2" section provides supplemental analysis cited in the 
> manuscript and the appendix.//
. 
. **GET THE DATASET**
. 
. //Download and save the file Study2.dta //
. //It is part of this replication package // 
. 
. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/Study2.dta"

. 
. //Of course your version of the dataset is saved differently. Go open it.//
. 
. **CLEAN THE DATA AND SET UP VARIABLES**
. 
. **1. Create comparison groups**
. //This is necessary in order to be able to compare each treatment group with 
> the control group//
. 
. gen whitecompare=.
(1,702 missing values generated)

. replace whitecompare=1 if treatment=="MediaAnalysis-WhiteVoters"
(425 real changes made)

. replace whitecompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. gen stratcompare=.
(1,702 missing values generated)

. replace stratcompare=1 if treatment=="StrategicThinkingTreatment"
(426 real changes made)

. replace stratcompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. gen malecompare=.
(1,702 missing values generated)

. replace malecompare=1 if treatment=="MediaAnalysis-MaleVoters"
(427 real changes made)

. replace malecompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. **2. Create DVs **
. 
. //Start with WOMEN candidates//
. 
. replace warren=0 if warren==.
(1,006 real changes made)

. replace harris=0 if harris==.
(1,211 real changes made)

. replace buttigieg=0 if buttigieg==.
(1,324 real changes made)

. replace booker=0 if booker==.
(1,356 real changes made)

. replace klobuchar=0 if klobuchar==.
(1,577 real changes made)

. replace biden=0 if biden==.
(343 real changes made)

. replace sanders=0 if sanders==.
(523 real changes made)

. replace orourke=0 if orourke==.
(1,170 real changes made)

. 
. //Top choice is a woman (binary)//
. 
. gen bestwoman1=0

. replace bestwoman1=1 if warren==1
(108 real changes made)

. replace bestwoman1=1 if harris==1
(74 real changes made)

. replace bestwoman1=1 if klobuchar==1
(17 real changes made)

. 
. //Total number of women in the top 3//
. gen bestwarrentop3=0

. replace bestwarrentop3=1 if warren>0
(696 real changes made)

. gen bestharristop3=0

. replace bestharristop3=1 if harris>0
(491 real changes made)

. gen bestklobuchartop3=0

. replace bestklobuchartop3=1 if klobuchar>0
(125 real changes made)

. gen bestwomantotal=bestwarrentop3+bestharristop3+bestklobuchartop3

. 
. //Are any women in the top 3 (binary)?//
. gen bestwomanbinary=0

. replace bestwomanbinary=1 if klobuchar>0
(125 real changes made)

. replace bestwomanbinary=1 if warren>0
(669 real changes made)

. replace bestwomanbinary=1 if harris>0
(314 real changes made)

. 
. //Now turn to BLACK candidates//
. 
. replace booker=0 if booker==.
(0 real changes made)

. 
. //Black candidate is top choice (binary)//
. gen bestblack1=0

. replace bestblack1=1 if harris==1
(74 real changes made)

. replace bestblack1=1 if booker==1
(41 real changes made)

. 
. //Total number of black candidates in top 3//
. gen bestbookertop3=0

. replace bestbookertop3=1 if booker>0
(346 real changes made)

. gen bestblacktotal=bestbookertop3+bestharristop3

. 
. //Are there any black candidates in the top 3?//
. gen bestblackbinary=0

. replace bestblackbinary=1 if harris>0
(491 real changes made)

. replace bestblackbinary=1 if booker>0
(272 real changes made)

. 
. //Now create CANDIDATE-SPECIFIC DVs//
. 
. //Make binary variables recording whether each candidate is in the #1 positio
> n//
. gen biden1=0

. replace biden1=1 if biden==1
(858 real changes made)

. 
. gen sanders1=0

. replace sanders1=1 if sanders==1
(467 real changes made)

. 
. gen warren1=0

. replace warren1=1 if warren==1
(108 real changes made)

. 
. gen harris1=0

. replace harris1=1 if harris==1
(74 real changes made)

. 
. gen booker1=0

. replace booker1=1 if booker==1
(41 real changes made)

. 
. gen klobuchar1=0

. replace klobuchar1=1 if klobuchar==1
(17 real changes made)

. 
. gen buttigieg1=0

. replace buttigieg1=1 if buttigieg==1
(55 real changes made)

. 
. gen orourke1=0

. replace orourke1=1 if orourke==1
(82 real changes made)

. 
. //Create binary variables recording whether each candidate is in the top3//
. 
. rename bestharristop3 harristop3

. rename bestwarrentop3 warrentop3

. rename bestbookertop3 bookertop3

. rename bestklobuchartop3 klobuchartop3

. 
. gen bidentop3=0

. replace bidentop3=1 if biden>0
(1,359 real changes made)

. 
. gen sanderstop3=0

. replace sanderstop3=1 if sanders>0
(1,179 real changes made)

. 
. gen buttigiegtop3=0

. replace buttigiegtop3=1 if buttigieg>0
(378 real changes made)

. 
. gen orourketop3=0

. replace orourketop3=1 if orourke>0
(532 real changes made)

. 
. **Generate dummy variables to indicate which subjects in the "strategic think
> ing" treatment**
. **had high estimates of racism & sexism, and which had low estimates**
. 
. gen woman35=.
(1,702 missing values generated)

. replace woman35=0 if stratcomp==1
(426 real changes made)

. replace woman35=1 if stratcomp==1 & notvotewoman>34
(215 real changes made)

. **The variable woman35 is coded 1 if the subjects said that 35% or more of sw
> ing-state voters would not vote**
. **for a woman for president. I chose the number 35 because it is the median (
> the mean is slightly higher).**
. 
. gen woman15=. 
(1,702 missing values generated)

. replace woman15=0 if stratcomp==1
(426 real changes made)

. replace woman15=1 if stratcomp==1 & notvotewoman>15
(357 real changes made)

. 
. gen woman25=. 
(1,702 missing values generated)

. replace woman25=0 if stratcomp==1
(426 real changes made)

. replace woman25=1 if stratcomp==1 & notvotewoman>24
(306 real changes made)

. 
. **Same logic, for black candidates**
. 
. gen black35=.
(1,702 missing values generated)

. replace black35=0 if stratcomp==1
(426 real changes made)

. replace black35=1 if notvoteblack>34 & stratcomp==1
(201 real changes made)

. 
. gen black15=. 
(1,702 missing values generated)

. replace black15=0 if stratcomp==1
(426 real changes made)

. replace black15=1 if stratcomp==1 & notvoteblack>15
(351 real changes made)

. 
. gen black25=. 
(1,702 missing values generated)

. replace black25=0 if stratcomp==1
(426 real changes made)

. replace black25=1 if stratcomp==1 & notvoteblack>24
(292 real changes made)

. 
. **Code Subject Demographics**
. 
. gen male=0 if gender!="Male"
(845 missing values generated)

. replace male=1 if gender=="Male"
(845 real changes made)

. 
. gen female=0 if gender!="Female"
(847 missing values generated)

. replace female=1 if gender=="Female"
(847 real changes made)

. 
. gen white=0 

. replace white=1 if race=="White / Caucasian" 
(1,188 real changes made)

. 
. gen black=0

. replace black=1 if race=="Black or African American"
(184 real changes made)

. 
. gen api=0

. replace api=1 if race=="Asian / Pacific Islander"
(152 real changes made)

. 
. gen hispanic=0

. replace hispanic=1 if race=="Hispanic or Latino"
(68 real changes made)

. 
. gen other=0

. replace other=1 if hispanic==0 & api==0 & black==0 & white==0
(110 real changes made)

. 
. gen agegroup=1 if age=="18 - 24 years old"
(1,556 missing values generated)

. replace agegroup=2 if age=="25 - 34 years old"
(713 real changes made)

. replace agegroup=3 if age=="35 - 44 years old"
(431 real changes made)

. replace agegroup=4 if age=="45 - 54 years old"
(215 real changes made)

. replace agegroup=5 if age=="55 - 64 years old"
(138 real changes made)

. replace agegroup=6 if age=="65 - 74 years old"
(54 real changes made)

. replace agegroup=7 if age=="75 years or older"
(5 real changes made)

. 
. *****************************************************************************
> ***
. *********ANALYSIS 1**********************************************************
> ***
. *****************************************************************************
> ***
. 
. //for TABLE 2.2//
. **Male Voters Treatment**
. ttest bestwomanbin, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .7051887    .0221694    .4564965    .6616127    .7487646
       1 |     427    .5644028    .0240233    .4964166    .5171839    .6116217
---------+--------------------------------------------------------------------
combined |     851    .6345476    .0165173      .48184    .6021282     .666967
---------+--------------------------------------------------------------------
    diff |            .1407859    .0326895                .0766239    .2049479
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   4.3068
Ho: diff = 0                             Welch's degrees of freedom =  846.022

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest bestwoman1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .1556604     .017627    .3629613     .121013    .1903077
       1 |     427    .0772834    .0129382    .2673538    .0518528    .1027139
---------+--------------------------------------------------------------------
combined |     851    .1163337    .0109973    .3208132    .0947486    .1379188
---------+--------------------------------------------------------------------
    diff |             .078377    .0218656                .0354545    .1212995
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.5845
Ho: diff = 0                             Welch's degrees of freedom =  779.154

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9998         Pr(|T| > |t|) = 0.0004          Pr(T > t) = 0.0002

. ttest bestwomantot, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .8537736     .031937     .657623    .7909986    .9165486
       1 |     427    .6393443    .0300684    .6213325    .5802434    .6984451
---------+--------------------------------------------------------------------
combined |     851     .746181    .0222211     .648233    .7025662    .7897957
---------+--------------------------------------------------------------------
    diff |            .2144293    .0438643                .1283339    .3005248
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   4.8885
Ho: diff = 0                             Welch's degrees of freedom =  847.549

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. //for Table 2.3//
. **White Voters Treatment**
. ttest bestblackbin, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424     .490566    .0243065    .5005016    .4427895    .5383426
       1 |     425    .4070588     .023859     .491865    .3601623    .4539554
---------+--------------------------------------------------------------------
combined |     849    .4487633    .0170797     .497661    .4152398    .4822867
---------+--------------------------------------------------------------------
    diff |            .0835072    .0340596                .0166563    .1503581
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.4518
Ho: diff = 0                             Welch's degrees of freedom =  848.668

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9928         Pr(|T| > |t|) = 0.0144          Pr(T > t) = 0.0072

. ttest bestblack1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0919811    .0140516    .2893407    .0643614    .1196008
       1 |     425         .04    .0095166    .1961901    .0212944    .0587056
---------+--------------------------------------------------------------------
combined |     849      .06596    .0085236    .2483584    .0492301    .0826899
---------+--------------------------------------------------------------------
    diff |            .0519811     .016971                .0186645    .0852977
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.0629
Ho: diff = 0                             Welch's degrees of freedom =  745.416

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9989         Pr(|T| > |t|) = 0.0023          Pr(T > t) = 0.0011

. ttest bestblacktot, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .5448113    .0290301    .5977669    .4877501    .6018726
       1 |     425    .4635294    .0292037    .6020498    .4061274    .5209315
---------+--------------------------------------------------------------------
combined |     849    .5041225    .0206241    .6009383    .4636421    .5446029
---------+--------------------------------------------------------------------
    diff |            .0812819    .0411777                .0004598     .162104
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.9739
Ho: diff = 0                             Welch's degrees of freedom =  848.981

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9756         Pr(|T| > |t|) = 0.0487          Pr(T > t) = 0.0244

. 
. //for Table 2.4//
. **Estimate Others' Biases Treatment**
. ttest bestwomanbin, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .7051887    .0221694    .4564965    .6616127    .7487646
       1 |     426    .6737089    .0227428    .4694065    .6290065    .7184113
---------+--------------------------------------------------------------------
combined |     850    .6894118     .015881    .4630069    .6582411    .7205824
---------+--------------------------------------------------------------------
    diff |            .0314798    .0317603               -.0308582    .0938177
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.9912
Ho: diff = 0                             Welch's degrees of freedom =  849.542

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8391         Pr(|T| > |t|) = 0.3219          Pr(T > t) = 0.1609

. ttest bestwomantot, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .8537736     .031937     .657623    .7909986    .9165486
       1 |     426    .8051643    .0314282    .6486704    .7433903    .8669383
---------+--------------------------------------------------------------------
combined |     850    .8294118    .0224052    .6532195    .7854356    .8733879
---------+--------------------------------------------------------------------
    diff |            .0486093    .0448074               -.0393369    .1365554
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.0848
Ho: diff = 0                             Welch's degrees of freedom =  849.711

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8609         Pr(|T| > |t|) = 0.2783          Pr(T > t) = 0.1391

. ttest bestwoman1, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .1556604     .017627    .3629613     .121013    .1903077
       1 |     426     .129108    .0162654    .3357137    .0971374    .1610786
---------+--------------------------------------------------------------------
combined |     850    .1423529    .0119918    .3496175    .1188159      .16589
---------+--------------------------------------------------------------------
    diff |            .0265524    .0239848               -.0205245    .0736293
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.1070
Ho: diff = 0                             Welch's degrees of freedom =  844.231

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8657         Pr(|T| > |t|) = 0.2686          Pr(T > t) = 0.1343

. ttest bestblackbin, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424     .490566    .0243065    .5005016    .4427895    .5383426
       1 |     426    .4389671    .0240722    .4968445    .3916518    .4862825
---------+--------------------------------------------------------------------
combined |     850    .4647059    .0171172    .4990464     .431109    .4983028
---------+--------------------------------------------------------------------
    diff |            .0515989    .0342093               -.0155457    .1187435
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.5083
Ho: diff = 0                             Welch's degrees of freedom =  849.877

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9341         Pr(|T| > |t|) = 0.1318          Pr(T > t) = 0.0659

. ttest bestblacktot, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .5448113    .0290301    .5977669    .4877501    .6018726
       1 |     426    .4812207    .0280401    .5787405    .4261062    .5363351
---------+--------------------------------------------------------------------
combined |     850    .5129412    .0201964    .5888217    .4733004    .5525819
---------+--------------------------------------------------------------------
    diff |            .0635907    .0403608                -.015628    .1428093
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.5756
Ho: diff = 0                             Welch's degrees of freedom =  848.833

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9422         Pr(|T| > |t|) = 0.1155          Pr(T > t) = 0.0578

. ttest bestblack1, by(stratcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0919811    .0140516    .2893407    .0643614    .1196008
       1 |     426      .07277    .0126001    .2600639    .0480036    .0975363
---------+--------------------------------------------------------------------
combined |     850    .0823529    .0094346    .2750635    .0638351    .1008708
---------+--------------------------------------------------------------------
    diff |            .0192112    .0188736               -.0178337    .0562561
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.0179
Ho: diff = 0                             Welch's degrees of freedom =   839.64

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8455         Pr(|T| > |t|) = 0.3090          Pr(T > t) = 0.1545

. 
. *****************************************************************************
> ***
. *********ANALYSIS 2**********************************************************
> ***
. *****************************************************************************
> ***
. 
. //APPENDIX TABLE 1.33//
. //Estimates of others' racism/sexism, by subject demographics//
. 
. **Who over-estimates sexism most?**
. sum notvotewoman if male==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        213    35.11737    22.05076          0        100

. sum notvotewoman if female==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        210    42.24286    23.34192          0        100

. 
. sum notvotewoman if white==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        274    37.27007     21.9074          0        100

. sum notvotewoman if black==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |         57    42.61404    27.07579          0        100

. sum notvotewoman if hispanic==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |         24       43.25    24.51486          0         89

. sum notvotewoman if api==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |         38    39.39474    22.10606          7         92

. 
. sum notvotewoman if agegr<3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        232    39.60345    22.70339          0        100

. sum notvotewoman if agegr>2 & agegr<5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        142    36.21127    24.08343          0        100

. sum notvotewoman if agegr>4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |         52    40.07692    20.59207          8         90

. 
. **Who over-estimates racism most?**
. sum notvoteblack if male==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        213    33.97653    21.51775          0        100

. sum notvoteblack if female==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        210    40.70476    23.93609          0        100

. 
. sum notvoteblack if white==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        274    34.93066    20.99448          0        100

. sum notvoteblack if black==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |         57    45.10526    28.79651          0        100

. sum notvoteblack if hispanic==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |         24    42.70833     24.8342          0         95

. sum notvoteblack if api==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |         38    41.57895    22.60427          4        100

. 
. sum notvoteblack if agegr<3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        232    37.46121    22.58391          0        100

. sum notvoteblack if agegr>2 & agegr<5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        142    35.76761     24.1745          0        100

. sum notvoteblack if agegr>4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |         52    41.32692    22.26905          8        100

. 
. //APPENDIX TABLE 1.34//
. //Subject demographics//
. 
. tab agegr

   agegroup |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        146        8.58        8.58
          2 |        713       41.89       50.47
          3 |        431       25.32       75.79
          4 |        215       12.63       88.43
          5 |        138        8.11       96.53
          6 |         54        3.17       99.71
          7 |          5        0.29      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab female

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        855       50.24       50.24
          1 |        847       49.76      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab male

       male |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        857       50.35       50.35
          1 |        845       49.65      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab other

      other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,592       93.54       93.54
          1 |        110        6.46      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab white

      white |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        514       30.20       30.20
          1 |      1,188       69.80      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab black

      black |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,518       89.19       89.19
          1 |        184       10.81      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab hispanic

   hispanic |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,634       96.00       96.00
          1 |         68        4.00      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. tab api

        api |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,550       91.07       91.07
          1 |        152        8.93      100.00
------------+-----------------------------------
      Total |      1,702      100.00

. 
. //After Table 2.4, the manuscript discusses heterogenous treatment effects//
. //across subjects with low and high estimates of others' sexism and racism.//
. 
. //That discussion is based on the following analysis.//
. 
. sum notvotewoman if stratcomp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvotewoman |        426    38.53052     22.9388          0        100

. sum notvoteblack if stratcomp==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
notvoteblack |        426    37.36854    23.09526          0        100

. 
. sum bestwomanbin if woman15==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |         69    .7826087    .4154928          0          1

. sum bestwomanbin if woman15==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |        357    .6526611    .4767928          0          1

. 
. sum bestwomanbin if woman25==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |        120    .7583333    .4298883          0          1

. sum bestwomanbin if woman25==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |        306    .6405229    .4806332          0          1

. 
. sum bestwomanbin if woman35==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |        211    .7251185    .4475163          0          1

. sum bestwomanbin if woman35==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestwomanb~y |        215    .6232558    .4857008          0          1

. 
. sum bestblackbin if black15==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |         75         .48    .5029642          0          1

. sum bestblackbin if black15==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |        351    .4301994    .4958107          0          1

. 
. sum bestblackbin if black25==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |        134    .4477612    .4991295          0          1

. sum bestblackbin if black25==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |        292    .4349315    .4965991          0          1

. 
. sum bestblackbin if black35==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |        225    .4666667          .5          0          1

. sum bestblackbin if black35==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
bestblackb~y |        201    .4079602    .4926828          0          1

. 
. //That's the end of this do-file.//
. //For candidate-specific results used to make Figures 2.1 and 2.2, see the do
> -file Study2_Figure.do //
. 
. clear 

. 
end of do-file

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the Study 2 results used to make Figur
> es 2.1 and 2.2//
. 
. //First, the do-file cleans and re-organizes the dataset.//
. //Then, the "Analysis" section provides the results for Figures 2.1 and 2.2./
> /
. //Based on these results, I hand-drew Figures 2.1 and 2.2 using Adobe Illustr
> ator.//
. 
. **GET THE DATASET**
. 
. //Download and save the file Study2.dta //
. //It is part of this replication package // 
. 
. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/Study2.dta"

. 
. //Of course your version of the dataset is saved differently. Go open it.//
. 
. **CLEAN THE DATA AND SET UP VARIABLES**
. 
. **1. Create comparison groups**
. //This is necessary in order to be able to compare each treatment group with 
> the control group//
. 
. gen whitecompare=.
(1,702 missing values generated)

. replace whitecompare=1 if treatment=="MediaAnalysis-WhiteVoters"
(425 real changes made)

. replace whitecompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. gen stratcompare=.
(1,702 missing values generated)

. replace stratcompare=1 if treatment=="StrategicThinkingTreatment"
(426 real changes made)

. replace stratcompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. gen malecompare=.
(1,702 missing values generated)

. replace malecompare=1 if treatment=="MediaAnalysis-MaleVoters"
(427 real changes made)

. replace malecompare=0 if treatment=="ControlConclusion"
(424 real changes made)

. 
. **2. Create DVs **
. 
. //Start with WOMEN candidates//
. 
. replace warren=0 if warren==.
(1,006 real changes made)

. replace harris=0 if harris==.
(1,211 real changes made)

. replace buttigieg=0 if buttigieg==.
(1,324 real changes made)

. replace booker=0 if booker==.
(1,356 real changes made)

. replace klobuchar=0 if klobuchar==.
(1,577 real changes made)

. replace biden=0 if biden==.
(343 real changes made)

. replace sanders=0 if sanders==.
(523 real changes made)

. replace orourke=0 if orourke==.
(1,170 real changes made)

. 
. //Top choice is a woman (binary)//
. 
. gen bestwoman1=0

. replace bestwoman1=1 if warren==1
(108 real changes made)

. replace bestwoman1=1 if harris==1
(74 real changes made)

. replace bestwoman1=1 if klobuchar==1
(17 real changes made)

. 
. //Total number of women in the top 3//
. gen bestwarrentop3=0

. replace bestwarrentop3=1 if warren>0
(696 real changes made)

. gen bestharristop3=0

. replace bestharristop3=1 if harris>0
(491 real changes made)

. gen bestklobuchartop3=0

. replace bestklobuchartop3=1 if klobuchar>0
(125 real changes made)

. gen bestwomantotal=bestwarrentop3+bestharristop3+bestklobuchartop3

. 
. //Are any women in the top 3 (binary)?//
. gen bestwomanbinary=0

. replace bestwomanbinary=1 if klobuchar>0
(125 real changes made)

. replace bestwomanbinary=1 if warren>0
(669 real changes made)

. replace bestwomanbinary=1 if harris>0
(314 real changes made)

. 
. //Now turn to BLACK candidates//
. 
. replace booker=0 if booker==.
(0 real changes made)

. 
. //Black candidate is top choice (binary)//
. gen bestblack1=0

. replace bestblack1=1 if harris==1
(74 real changes made)

. replace bestblack1=1 if booker==1
(41 real changes made)

. 
. //Total number of black candidates in top 3//
. gen bestbookertop3=0

. replace bestbookertop3=1 if booker>0
(346 real changes made)

. gen bestblacktotal=bestbookertop3+bestharristop3

. 
. //Are there any black candidates in the top 3?//
. gen bestblackbinary=0

. replace bestblackbinary=1 if harris>0
(491 real changes made)

. replace bestblackbinary=1 if booker>0
(272 real changes made)

. 
. //Now create CANDIDATE-SPECIFIC DVs//
. 
. //Make binary variables recording whether each candidate is in the #1 positio
> n//
. gen biden1=0

. replace biden1=1 if biden==1
(858 real changes made)

. 
. gen sanders1=0

. replace sanders1=1 if sanders==1
(467 real changes made)

. 
. gen warren1=0

. replace warren1=1 if warren==1
(108 real changes made)

. 
. gen harris1=0

. replace harris1=1 if harris==1
(74 real changes made)

. 
. gen booker1=0

. replace booker1=1 if booker==1
(41 real changes made)

. 
. gen klobuchar1=0

. replace klobuchar1=1 if klobuchar==1
(17 real changes made)

. 
. gen buttigieg1=0

. replace buttigieg1=1 if buttigieg==1
(55 real changes made)

. 
. gen orourke1=0

. replace orourke1=1 if orourke==1
(82 real changes made)

. 
. //Create binary variables recording whether each candidate is in the top3//
. 
. rename bestharristop3 harristop3

. rename bestwarrentop3 warrentop3

. rename bestbookertop3 bookertop3

. rename bestklobuchartop3 klobuchartop3

. 
. gen bidentop3=0

. replace bidentop3=1 if biden>0
(1,359 real changes made)

. 
. gen sanderstop3=0

. replace sanderstop3=1 if sanders>0
(1,179 real changes made)

. 
. gen buttigiegtop3=0

. replace buttigiegtop3=1 if buttigieg>0
(378 real changes made)

. 
. gen orourketop3=0

. replace orourketop3=1 if orourke>0
(532 real changes made)

. 
. **Generate dummy variables to indicate which subjects in the "strategic think
> ing" treatment**
. **had high estimates of racism & sexism, and which had low estimates**
. 
. gen woman35=.
(1,702 missing values generated)

. replace woman35=0 if stratcomp==1
(426 real changes made)

. replace woman35=1 if stratcomp==1 & notvotewoman>34
(215 real changes made)

. **The variable woman35 is coded 1 if the subjects said that 35% or more of sw
> ing-state voters would not vote**
. **for a woman for president. I chose the number 35 because it is the median (
> the mean is slightly higher).**
. 
. gen woman15=. 
(1,702 missing values generated)

. replace woman15=0 if stratcomp==1
(426 real changes made)

. replace woman15=1 if stratcomp==1 & notvotewoman>15
(357 real changes made)

. 
. gen woman25=. 
(1,702 missing values generated)

. replace woman25=0 if stratcomp==1
(426 real changes made)

. replace woman25=1 if stratcomp==1 & notvotewoman>24
(306 real changes made)

. 
. **Same logic, for black candidates**
. 
. gen black35=.
(1,702 missing values generated)

. replace black35=0 if stratcomp==1
(426 real changes made)

. replace black35=1 if notvoteblack>34 & stratcomp==1
(201 real changes made)

. 
. gen black15=. 
(1,702 missing values generated)

. replace black15=0 if stratcomp==1
(426 real changes made)

. replace black15=1 if stratcomp==1 & notvoteblack>15
(351 real changes made)

. 
. gen black25=. 
(1,702 missing values generated)

. replace black25=0 if stratcomp==1
(426 real changes made)

. replace black25=1 if stratcomp==1 & notvoteblack>24
(292 real changes made)

. 
. **Code Subject Demographics**
. 
. gen male=0 if gender!="Male"
(845 missing values generated)

. replace male=1 if gender=="Male"
(845 real changes made)

. 
. gen female=0 if gender!="Female"
(847 missing values generated)

. replace female=1 if gender=="Female"
(847 real changes made)

. 
. gen white=0 

. replace white=1 if race=="White / Caucasian" 
(1,188 real changes made)

. 
. gen black=0

. replace black=1 if race=="Black or African American"
(184 real changes made)

. 
. gen api=0

. replace api=1 if race=="Asian / Pacific Islander"
(152 real changes made)

. 
. gen hispanic=0

. replace hispanic=1 if race=="Hispanic or Latino"
(68 real changes made)

. 
. gen other=0

. replace other=1 if hispanic==0 & api==0 & black==0 & white==0
(110 real changes made)

. 
. gen agegroup=1 if age=="18 - 24 years old"
(1,556 missing values generated)

. replace agegroup=2 if age=="25 - 34 years old"
(713 real changes made)

. replace agegroup=3 if age=="35 - 44 years old"
(431 real changes made)

. replace agegroup=4 if age=="45 - 54 years old"
(215 real changes made)

. replace agegroup=5 if age=="55 - 64 years old"
(138 real changes made)

. replace agegroup=6 if age=="65 - 74 years old"
(54 real changes made)

. replace agegroup=7 if age=="75 years or older"
(5 real changes made)

. 
. *****************************************************************************
> ***
. *********ANALYSIS************************************************************
> *
. *****************************************************************************
> ***
. 
. //for FIGURES 2.1 and 2.2//
. **Candidate-specific ATEs**
. 
. ttest harris1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0754717    .0128435     .264463    .0502268    .1007166
       1 |     427    .0351288    .0089199    .1843213    .0175962    .0526614
---------+--------------------------------------------------------------------
combined |     851    .0552291     .007835    .2285613     .039851    .0706073
---------+--------------------------------------------------------------------
    diff |            .0403429    .0156371                .0096456    .0710402
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.5799
Ho: diff = 0                             Welch's degrees of freedom =  756.614

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9950         Pr(|T| > |t|) = 0.0101          Pr(T > t) = 0.0050

. ttest harristop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .3396226    .0230263    .4741406    .2943624    .3848829
       1 |     427     .236534    .0205891    .4254522    .1960651    .2770028
---------+--------------------------------------------------------------------
combined |     851    .2878966    .0155303    .4530489    .2574143    .3183789
---------+--------------------------------------------------------------------
    diff |            .1030887    .0308888                .0424603    .1637171
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.3374
Ho: diff = 0                             Welch's degrees of freedom =   839.88

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9996         Pr(|T| > |t|) = 0.0009          Pr(T > t) = 0.0004

. ttest warren1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0707547    .0124673    .2567176    .0462491    .0952603
       1 |     427    .0327869    .0086279    .1782873    .0158283    .0497455
---------+--------------------------------------------------------------------
combined |     851    .0517039    .0075949    .2215587    .0367969    .0666109
---------+--------------------------------------------------------------------
    diff |            .0379678    .0151616                .0082039    .0677318
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.5042
Ho: diff = 0                             Welch's degrees of freedom =  755.124

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9938         Pr(|T| > |t|) = 0.0125          Pr(T > t) = 0.0062

. ttest warrentop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .4268868    .0240495    .4952099    .3796153    .4741582
       1 |     427    .3348946    .0228662    .4725072    .2899499    .3798393
---------+--------------------------------------------------------------------
combined |     851    .3807286    .0166548    .4858515    .3480392    .4134179
---------+--------------------------------------------------------------------
    diff |            .0919922     .033185                .0268579    .1571265
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.7721
Ho: diff = 0                             Welch's degrees of freedom =  848.526

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9972         Pr(|T| > |t|) = 0.0057          Pr(T > t) = 0.0028

. ttest biden1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .4882075    .0243041    .5004514    .4404358    .5359793
       1 |     427    .5503513    .0241019    .4980418    .5029778    .5977248
---------+--------------------------------------------------------------------
combined |     851     .519389     .017137    .4999177    .4857532    .5530247
---------+--------------------------------------------------------------------
    diff |           -.0621437    .0342285                -.129326    .0050385
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.8156
Ho: diff = 0                             Welch's degrees of freedom =   850.88

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0349         Pr(|T| > |t|) = 0.0698          Pr(T > t) = 0.9651

. ttest bidentop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .7806604    .0201196     .414288    .7411135    .8202072
       1 |     427    .8594848    .0168374    .3479284      .82639    .8925796
---------+--------------------------------------------------------------------
combined |     851    .8202115    .0131715     .384237    .7943591    .8460639
---------+--------------------------------------------------------------------
    diff |           -.0788244    .0262354               -.1303205   -.0273283
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.0045
Ho: diff = 0                             Welch's degrees of freedom =  824.302

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0014         Pr(|T| > |t|) = 0.0027          Pr(T > t) = 0.9986

. ttest sanders1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2712264    .0216168    .4451179    .2287366    .3137162
       1 |     427    .2693208    .0214928    .4441273    .2270757     .311566
---------+--------------------------------------------------------------------
combined |     851    .2702703    .0152325    .4443605    .2403726     .300168
---------+--------------------------------------------------------------------
    diff |            .0019056    .0304833               -.0579256    .0617368
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0625
Ho: diff = 0                             Welch's degrees of freedom =  850.927

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5249         Pr(|T| > |t|) = 0.9502          Pr(T > t) = 0.4751

. ttest sanderstop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .6957547    .0223702    .4606306    .6517841    .7397253
       1 |     427    .6861827     .022483    .4645874    .6419913     .730374
---------+--------------------------------------------------------------------
combined |     851    .6909518    .0158499    .4623728    .6598422    .7220614
---------+--------------------------------------------------------------------
    diff |             .009572    .0317161               -.0526789    .0718229
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.3018
Ho: diff = 0                             Welch's degrees of freedom =  850.998

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6186         Pr(|T| > |t|) = 0.7629          Pr(T > t) = 0.3814

. ttest orourke1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0400943    .0095386    .1964122    .0213453    .0588433
       1 |     427    .0491803    .0104771     .216498    .0285871    .0697735
---------+--------------------------------------------------------------------
combined |     851    .0446533    .0070843     .206663    .0307485    .0585582
---------+--------------------------------------------------------------------
    diff |            -.009086    .0141688               -.0368962    .0187242
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6413
Ho: diff = 0                             Welch's degrees of freedom =  844.139

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2608         Pr(|T| > |t|) = 0.5215          Pr(T > t) = 0.7392

. ttest orourketop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2665094    .0214973    .4426559    .2242547    .3087642
       1 |     427    .3489461    .0230931     .477196    .3035555    .3943368
---------+--------------------------------------------------------------------
combined |     851    .3078731    .0158332    .4618852    .2767963    .3389499
---------+--------------------------------------------------------------------
    diff |           -.0824367    .0315504               -.1443628   -.0205106
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.6129
Ho: diff = 0                             Welch's degrees of freedom =  847.077

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0046         Pr(|T| > |t|) = 0.0091          Pr(T > t) = 0.9954

. ttest buttigieg1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0283019    .0080631    .1660298    .0124531    .0441507
       1 |     427    .0234192    .0073272    .1514082    .0090173    .0378211
---------+--------------------------------------------------------------------
combined |     851    .0258519    .0054431    .1587868    .0151684    .0365355
---------+--------------------------------------------------------------------
    diff |            .0048827     .010895               -.0165018    .0262672
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.4482
Ho: diff = 0                             Welch's degrees of freedom =  842.731

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6729         Pr(|T| > |t|) = 0.6542          Pr(T > t) = 0.3271

. ttest buttigiegtop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .1981132    .0193795    .3990488    .1600211    .2362054
       1 |     427    .2248244    .0202263    .4179563    .1850685    .2645802
---------+--------------------------------------------------------------------
combined |     851    .2115159    .0140074    .4086234    .1840227    .2390091
---------+--------------------------------------------------------------------
    diff |           -.0267111     .028012               -.0816919    .0282696
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.9536
Ho: diff = 0                             Welch's degrees of freedom =   849.69

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1703         Pr(|T| > |t|) = 0.3406          Pr(T > t) = 0.8297

. ttest booker1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0165094    .0061956    .1275745    .0043315    .0286874
       1 |     427     .030445    .0083241    .1720098    .0140835    .0468065
---------+--------------------------------------------------------------------
combined |     851    .0235018    .0051961    .1515798    .0133031    .0337004
---------+--------------------------------------------------------------------
    diff |           -.0139355    .0103767               -.0343048    .0064338
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.3430
Ho: diff = 0                             Welch's degrees of freedom =  787.537

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0898         Pr(|T| > |t|) = 0.1797          Pr(T > t) = 0.9102

. ttest bookertop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2051887    .0196353    .4043165    .1665937    .2437837
       1 |     427    .2412178     .020728     .428324    .2004758    .2819598
---------+--------------------------------------------------------------------
combined |     851    .2232667    .0142836    .4166806    .1952314    .2513021
---------+--------------------------------------------------------------------
    diff |           -.0360291    .0285517               -.0920693    .0200111
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.2619
Ho: diff = 0                             Welch's degrees of freedom =  848.823

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1037         Pr(|T| > |t|) = 0.2073          Pr(T > t) = 0.8963

. ttest klobuchar1, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424     .009434    .0047002    .0967835    .0001953    .0186727
       1 |     427    .0093677    .0046673    .0964454    .0001938    .0185415
---------+--------------------------------------------------------------------
combined |     851    .0094007    .0033099    .0965572    .0029041    .0158973
---------+--------------------------------------------------------------------
    diff |            .0000663    .0066239               -.0129348    .0130674
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0100
Ho: diff = 0                             Welch's degrees of freedom =  850.905

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5040         Pr(|T| > |t|) = 0.9920          Pr(T > t) = 0.4960

. ttest klobuchartop3, by(malecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0872642    .0137221    .2825552    .0602922    .1142361
       1 |     427    .0679157    .0121901    .2518963    .0439554     .091876
---------+--------------------------------------------------------------------
combined |     851    .0775558    .0091742    .2676286    .0595491    .0955625
---------+--------------------------------------------------------------------
    diff |            .0193485    .0183547               -.0166781     .055375
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.0541
Ho: diff = 0                             Welch's degrees of freedom =  838.621

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8539         Pr(|T| > |t|) = 0.2921          Pr(T > t) = 0.1461

. 
. ttest harris1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0754717    .0128435     .264463    .0502268    .1007166
       1 |     425    .0258824    .0077112    .1589714    .0107253    .0410394
---------+--------------------------------------------------------------------
combined |     849    .0506478      .00753    .2194067    .0358682    .0654275
---------+--------------------------------------------------------------------
    diff |            .0495893    .0149806                .0201767     .079002
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.3102
Ho: diff = 0                             Welch's degrees of freedom =  694.361

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9995         Pr(|T| > |t|) = 0.0010          Pr(T > t) = 0.0005

. ttest harristop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .3396226    .0230263    .4741406    .2943624    .3848829
       1 |     425    .2870588      .02197    .4529224    .2438752    .3302424
---------+--------------------------------------------------------------------
combined |     849    .3133098    .0159283    .4641126    .2820463    .3445733
---------+--------------------------------------------------------------------
    diff |            .0525638    .0318259               -.0099031    .1150308
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.6516
Ho: diff = 0                             Welch's degrees of freedom =  847.036

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9505         Pr(|T| > |t|) = 0.0990          Pr(T > t) = 0.0495

. ttest warren1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0707547    .0124673    .2567176    .0462491    .0952603
       1 |     425    .0705882    .0124391    .2564376    .0461383    .0950381
---------+--------------------------------------------------------------------
combined |     849    .0706714    .0088005    .2564262     .053398    .0879447
---------+--------------------------------------------------------------------
    diff |            .0001665    .0176115               -.0344006    .0347336
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0095
Ho: diff = 0                             Welch's degrees of freedom =   848.99

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5038         Pr(|T| > |t|) = 0.9925          Pr(T > t) = 0.4962

. ttest warrentop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .4268868    .0240495    .4952099    .3796153    .4741582
       1 |     425    .4352941     .024078    .4963798    .3879671    .4826211
---------+--------------------------------------------------------------------
combined |     849    .4310954    .0170062    .4955213    .3977161    .4644747
---------+--------------------------------------------------------------------
    diff |           -.0084073    .0340313               -.0752026     .058388
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.2470
Ho: diff = 0                             Welch's degrees of freedom =      849

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4025         Pr(|T| > |t|) = 0.8049          Pr(T > t) = 0.5975

. ttest biden1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .4882075    .0243041    .5004514    .4404358    .5359793
       1 |     425    .4917647    .0242789    .5005214    .4440428    .5394866
---------+--------------------------------------------------------------------
combined |     849    .4899882    .0171666    .5001944    .4562942    .5236823
---------+--------------------------------------------------------------------
    diff |           -.0035572    .0343533               -.0709846    .0638702
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.1035
Ho: diff = 0                             Welch's degrees of freedom =  848.996

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4588         Pr(|T| > |t|) = 0.9176          Pr(T > t) = 0.5412

. ttest bidentop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .7806604    .0201196     .414288    .7411135    .8202072
       1 |     425         .76     .020741    .4275865     .719232     .800768
---------+--------------------------------------------------------------------
combined |     849     .770318    .0144444    .4208762     .741967    .7986691
---------+--------------------------------------------------------------------
    diff |            .0206604    .0288961               -.0360559    .0773767
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.7150
Ho: diff = 0                             Welch's degrees of freedom =  848.273

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7626         Pr(|T| > |t|) = 0.4748          Pr(T > t) = 0.2374

. ttest sanders1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2712264    .0216168    .4451179    .2287366    .3137162
       1 |     425    .2917647    .0220761    .4551103    .2483725    .3351569
---------+--------------------------------------------------------------------
combined |     849    .2815077    .0154439    .4499996    .2511948    .3118205
---------+--------------------------------------------------------------------
    diff |           -.0205383    .0308973               -.0811823    .0401057
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6647
Ho: diff = 0                             Welch's degrees of freedom =  848.665

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2532         Pr(|T| > |t|) = 0.5064          Pr(T > t) = 0.7468

. ttest sanderstop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .6957547    .0223702    .4606306    .6517841    .7397253
       1 |     425    .6988235    .0222798    .4593099    .6550309    .7426161
---------+--------------------------------------------------------------------
combined |     849    .6972909    .0157769    .4597012    .6663246    .7282573
---------+--------------------------------------------------------------------
    diff |           -.0030688    .0315724               -.0650379    .0589003
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.0972
Ho: diff = 0                             Welch's degrees of freedom =  848.977

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4613         Pr(|T| > |t|) = 0.9226          Pr(T > t) = 0.5387

. ttest orourke1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0400943    .0095386    .1964122    .0213453    .0588433
       1 |     425    .0494118    .0105252    .2169816    .0287238    .0700997
---------+--------------------------------------------------------------------
combined |     849    .0447585    .0071006    .2068952    .0308217    .0586954
---------+--------------------------------------------------------------------
    diff |           -.0093174    .0142044               -.0371976    .0185628
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6560
Ho: diff = 0                             Welch's degrees of freedom =  841.079

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2560         Pr(|T| > |t|) = 0.5120          Pr(T > t) = 0.7440

. ttest orourketop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2665094    .0214973    .4426559    .2242547    .3087642
       1 |     425         .32    .0226541    .4670259    .2754717    .3645283
---------+--------------------------------------------------------------------
combined |     849    .2932862     .015634    .4555369    .2626004    .3239721
---------+--------------------------------------------------------------------
    diff |           -.0534906    .0312304               -.1147887    .0078076
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.7128
Ho: diff = 0                             Welch's degrees of freedom =  846.776

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0436         Pr(|T| > |t|) = 0.0871          Pr(T > t) = 0.9564

. ttest buttigieg1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0283019    .0080631    .1660298    .0124531    .0441507
       1 |     425    .0470588    .0102842    .2120143    .0268445    .0672732
---------+--------------------------------------------------------------------
combined |     849    .0376914      .00654    .1905611    .0248548     .050528
---------+--------------------------------------------------------------------
    diff |           -.0187569    .0130682               -.0444088     .006895
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.4353
Ho: diff = 0                             Welch's degrees of freedom =  803.582

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0758         Pr(|T| > |t|) = 0.1516          Pr(T > t) = 0.9242

. ttest buttigiegtop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .1981132    .0193795    .3990488    .1600211    .2362054
       1 |     425    .2588235    .0212706    .4385047    .2170146    .3006325
---------+--------------------------------------------------------------------
combined |     849    .2285041    .0144184    .4201165    .2002043     .256804
---------+--------------------------------------------------------------------
    diff |           -.0607103    .0287751               -.1171896    -.004231
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.1098
Ho: diff = 0                             Welch's degrees of freedom =  841.909

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0176         Pr(|T| > |t|) = 0.0352          Pr(T > t) = 0.9824

. ttest booker1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0165094    .0061956    .1275745    .0043315    .0286874
       1 |     425    .0141176    .0057294    .1181151     .002856    .0253793
---------+--------------------------------------------------------------------
combined |     849    .0153121    .0042167    .1228635    .0070358    .0235885
---------+--------------------------------------------------------------------
    diff |            .0023918    .0084387               -.0141715    .0189551
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.2834
Ho: diff = 0                             Welch's degrees of freedom =  843.691

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6115         Pr(|T| > |t|) = 0.7769          Pr(T > t) = 0.3885

. ttest bookertop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .2051887    .0196353    .4043165    .1665937    .2437837
       1 |     425    .1764706    .0185137    .3816693    .1400806    .2128606
---------+--------------------------------------------------------------------
combined |     849    .1908127    .0134937    .3931734    .1643278    .2172976
---------+--------------------------------------------------------------------
    diff |            .0287181    .0269871               -.0242514    .0816876
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.0641
Ho: diff = 0                             Welch's degrees of freedom =  845.955

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8562         Pr(|T| > |t|) = 0.2876          Pr(T > t) = 0.1438

. ttest klobuchar1, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424     .009434    .0047002    .0967835    .0001953    .0186727
       1 |     425    .0094118    .0046892    .0966704    .0001948    .0186287
---------+--------------------------------------------------------------------
combined |     849    .0094229    .0033177    .0966699     .002911    .0159347
---------+--------------------------------------------------------------------
    diff |            .0000222    .0066393               -.0130092    .0130536
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0033
Ho: diff = 0                             Welch's degrees of freedom =  848.989

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5013         Pr(|T| > |t|) = 0.9973          Pr(T > t) = 0.4987

. ttest klobuchartop3, by(whitecomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     424    .0872642    .0137221    .2825552    .0602922    .1142361
       1 |     425    .0635294    .0118454    .2442002    .0402463    .0868125
---------+--------------------------------------------------------------------
combined |     849    .0753828    .0090661    .2641636    .0575882    .0931774
---------+--------------------------------------------------------------------
    diff |            .0237347    .0181276               -.0118466     .059316
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.3093
Ho: diff = 0                             Welch's degrees of freedom =  830.955

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9046         Pr(|T| > |t|) = 0.1908          Pr(T > t) = 0.0954

. 
. //These results are also discussed in the accompanying section of the manuscr
> ipt//
. 
. //That's all! Now use Illustrator to make the graphic.//
. //To replicate the results of Study 3, see the do-file "Study3_Analysis.do" /
> /
. 
. clear 

. 
end of do-file

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD80542.000000"

. **Project: Strategic Discrimination**
. **by Regina Bateson**
. **Last modified: 21 June 2020**
. 
. //This do-file provides the output for the Study 3 results//
. 
. //First, the do-file cleans and re-organizes the dataset.//
. //Then, the "Analysis 1" section provides the main results in the manuscript.
> //
. //Last, the "Analysis 2" section provides supplemental analysis cited in the 
> manuscript and the appendix.//
. 
. **GET THE DATASET**
. 
. //Download and save the file Study3.dta //
. //It is part of this replication package // 
. 
. use "/Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/Perspec
> tives Final Submission/Data and Replication Files/Study3.dta"

. 
. //Of course your version of the dataset is saved differently. Go open it.//
. 
. **CLEAN THE DATA AND SET UP VARIABLES**
. 
. **drop all subjects who did not pass the screening questions to participate i
> n the experiment**
. drop if treatment==""
(2,341 observations deleted)

. 
. **1. Create comparison groups**
. //This is necessary to be able to compare the treatment groups with the contr
> ol groups//
. gen undercompare=. 
(2,219 missing values generated)

. replace underc=1 if treatment=="Underwoodtreatment"
(438 real changes made)

. replace underc=0 if treatment=="Conclusion"
(445 real changes made)

. 
. gen shamingcompare=.
(2,219 missing values generated)

. replace shamingcompare=1 if treatment=="Namingandshamingtreatment"
(446 real changes made)

. replace shamingcompare=0 if treatment=="Conclusion"
(445 real changes made)

. 
. gen correctcompare=.
(2,219 missing values generated)

. replace correctcompare=1 if treatment=="Correctinformationtreatment"
(443 real changes made)

. replace correctcompare=0 if treatment=="Conclusion"
(445 real changes made)

. 
. gen blackcompare=.
(2,219 missing values generated)

. replace blackcompare=1 if treatment=="Blackvotersargument"
(447 real changes made)

. replace blackcompare=0 if treatment=="Conclusion"
(445 real changes made)

. 
. **2. Create DVs **
. 
. replace warren=0 if warren==.
(1,087 real changes made)

. replace harris=0 if harris==.
(1,441 real changes made)

. replace buttigieg=0 if buttigieg==.
(1,755 real changes made)

. replace booker=0 if booker==.
(1,789 real changes made)

. replace klobuchar=0 if klobuchar==.
(2,051 real changes made)

. replace biden=0 if biden==.
(477 real changes made)

. replace sanders=0 if sanders==.
(754 real changes made)

. replace orourke=0 if orourke==.
(1,741 real changes made)

. 
. //Top choice is a woman (binary)//
. gen bestwoman1=0

. replace bestwoman1=1 if warren==1
(225 real changes made)

. replace bestwoman1=1 if harris==1
(128 real changes made)

. replace bestwoman1=1 if klobuchar==1
(29 real changes made)

. 
. //Total number of women in the top 3//
. gen bestwarrentop3=0

. replace bestwarrentop3=1 if warren>0
(1,132 real changes made)

. gen bestharristop3=0

. replace bestharristop3=1 if harris>0
(778 real changes made)

. gen bestklobuchartop3=0

. replace bestklobuchartop3=1 if klobuchar>0
(168 real changes made)

. gen bestwomantotal=bestwarrentop3+bestharristop3+bestklobuchartop3

. 
. //Are any women in the top 3?//
. 
. gen bestwomanbinary=0

. replace bestwomanbinary=1 if klobuchar>0
(168 real changes made)

. replace bestwomanbinary=1 if warren>0
(1,089 real changes made)

. replace bestwomanbinary=1 if harris>0
(459 real changes made)

. 
. //BLACK candidate DVs//
. 
. replace booker=0 if booker==.
(0 real changes made)

. 
. **Black candidate is top choice (binary)**
. gen bestblack1=0

. replace bestblack1=1 if harris==1
(128 real changes made)

. replace bestblack1=1 if booker==1
(80 real changes made)

. 
. **Black candidates is in top 3**
. gen bestbookertop3=0

. replace bestbookertop3=1 if booker>0
(430 real changes made)

. gen bestblacktotal=bestbookertop3+bestharristop3

. 
. **Are there any black candidates in the top 3?**
. gen bestblackbinary=0

. replace bestblackbinary=1 if harris>0
(778 real changes made)

. replace bestblackbinary=1 if booker>0
(291 real changes made)

. 
. **Make binary variables recording whether each candidate is in the #1 positio
> n**
. gen biden1=0

. replace biden1=1 if biden==1
(1,082 real changes made)

. gen sanders1=0

. replace sanders1=1 if sanders==1
(522 real changes made)

. gen warren1=0

. replace warren1=1 if warren==1
(225 real changes made)

. gen harris1=0

. replace harris1=1 if harris==1
(128 real changes made)

. gen booker1=0

. replace booker1=1 if booker==1
(80 real changes made)

. gen klobuchar1=0

. replace klobuchar1=1 if klobuchar==1
(29 real changes made)

. gen buttigieg1=0

. replace buttigieg1=1 if buttigieg==1
(71 real changes made)

. gen orourke1=0

. replace orourke1=1 if orourke==1
(82 real changes made)

. 
. **Create binary variables recording whether each candidate is in the top3**
. 
. rename bestharristop3 harristop3

. rename bestwarrentop3 warrentop3

. rename bestbookertop3 bookertop3

. rename bestklobuchartop3 klobuchartop3

. gen bidentop3=0

. replace bidentop3=1 if biden>0
(1,742 real changes made)

. gen sanderstop3=0

. replace sanderstop3=1 if sanders>0
(1,465 real changes made)

. gen buttigiegtop3=0

. replace buttigiegtop3=1 if buttigieg>0
(464 real changes made)

. gen orourketop3=0

. replace orourketop3=1 if orourke>0
(478 real changes made)

. 
. //Code Subject Demographics//
. gen male=0 if gender!="Male"
(931 missing values generated)

. replace male=1 if gender=="Male"
(931 real changes made)

. 
. gen female=0 if gender!="Female"
(1,266 missing values generated)

. replace female=1 if gender=="Female"
(1,266 real changes made)

. 
. gen white=0 

. replace white=1 if race=="White / Caucasian" 
(1,522 real changes made)

. 
. gen black=0

. replace black=1 if race=="Black or African American"
(246 real changes made)

. 
. gen api=0

. replace api=1 if race=="Asian / Pacific Islander"
(144 real changes made)

. 
. gen hispanic=0

. replace hispanic=1 if race=="Hispanic or Latino"
(138 real changes made)

. 
. gen other=0

. replace other=1 if hispanic==0 & api==0 & black==0 & white==0
(169 real changes made)

. 
. gen agegroup=1 if age=="18 - 24 years old"
(1,929 missing values generated)

. replace agegroup=2 if age=="25 - 34 years old"
(951 real changes made)

. replace agegroup=3 if age=="35 - 44 years old"
(543 real changes made)

. replace agegroup=4 if age=="45 - 54 years old"
(240 real changes made)

. replace agegroup=5 if age=="55 - 64 years old"
(141 real changes made)

. replace agegroup=6 if age=="65 - 74 years old"
(48 real changes made)

. replace agegroup=7 if age=="75 years or older"
(6 real changes made)

. 
. *****************************************************************************
> ***
. *********ANALYSIS 1**********************************************************
> ***
. *****************************************************************************
> ***
. 
. //For Table 3.2//
. **Correct info treatment**
. 
. ttest bestblackbin, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .4202247     .023425    .4941504    .3741871    .4662623
       1 |     443    .4582393    .0236995    .4988163    .4116616    .5048169
---------+--------------------------------------------------------------------
combined |     888    .4391892    .0166637    .4965679    .4064843    .4718941
---------+--------------------------------------------------------------------
    diff |           -.0380146    .0333226               -.1034148    .0273857
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.1408
Ho: diff = 0                             Welch's degrees of freedom =  887.828

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1271         Pr(|T| > |t|) = 0.2543          Pr(T > t) = 0.8729

. ttest bestblacktot, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445     .458427    .0270417    .5704449    .4052814    .5115726
       1 |     443     .510158    .0282896    .5954281    .4545591    .5657569
---------+--------------------------------------------------------------------
combined |     888    .4842342    .0195739    .5832878    .4458178    .5226507
---------+--------------------------------------------------------------------
    diff |            -.051731    .0391351               -.1285394    .0250773
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.3219
Ho: diff = 0                             Welch's degrees of freedom =   886.01

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0933         Pr(|T| > |t|) = 0.1866          Pr(T > t) = 0.9067

. ttest bestblack1, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .0494382     .010288    .2170251     .029219    .0696574
       1 |     443    .0790068    .0128307    .2700543    .0537901    .1042235
---------+--------------------------------------------------------------------
combined |     888    .0641892    .0082293    .2452278     .048038    .0803404
---------+--------------------------------------------------------------------
    diff |           -.0295686    .0164459               -.0618481     .002711
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.7979
Ho: diff = 0                             Welch's degrees of freedom =   847.06

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0363         Pr(|T| > |t|) = 0.0725          Pr(T > t) = 0.9637

. 
. ttest bestwomanbin, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .7573034    .0203458    .4291957    .7173173    .7972895
       1 |     443     .738149    .0209116     .440139    .6970504    .7792476
---------+--------------------------------------------------------------------
combined |     888    .7477477    .0145825      .43455    .7191274    .7763681
---------+--------------------------------------------------------------------
    diff |            .0191544    .0291762                -.038108    .0764168
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.6565
Ho: diff = 0                             Welch's degrees of freedom =  887.217

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7442         Pr(|T| > |t|) = 0.5117          Pr(T > t) = 0.2558

. ttest bestwomantot, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .8741573    .0283637    .5983325    .8184136    .9299011
       1 |     443    .9006772    .0312837    .6584463    .8391939    .9621605
---------+--------------------------------------------------------------------
combined |     888    .8873874     .021102    .6288254    .8459717    .9288031
---------+--------------------------------------------------------------------
    diff |           -.0265199    .0422276               -.1093986    .0563588
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6280
Ho: diff = 0                             Welch's degrees of freedom =  879.201

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2651         Pr(|T| > |t|) = 0.5302          Pr(T > t) = 0.7349

. ttest bestwoman1, by(correctc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .1280899    .0158599     .334566      .09692    .1592598
       1 |     443    .1647856     .017646    .3714063     .130105    .1994661
---------+--------------------------------------------------------------------
combined |     888    .1463964    .0118695    .3537024    .1231009    .1696919
---------+--------------------------------------------------------------------
    diff |           -.0366957     .023726               -.0832619    .0098706
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.5466
Ho: diff = 0                             Welch's degrees of freedom =  877.633

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0612         Pr(|T| > |t|) = 0.1223          Pr(T > t) = 0.9388

. 
. //For Table 3.3//
. **Naming and Shaming Treatment**
. 
. ttest bestblackbin, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .4202247     .023425    .4941504    .3741871    .4662623
       1 |     446    .4349776     .023501    .4963108    .3887909    .4811643
---------+--------------------------------------------------------------------
combined |     891    .4276094    .0165834    .4950097    .3950622    .4601567
---------+--------------------------------------------------------------------
    diff |           -.0147529    .0331817               -.0798763    .0503706
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.4446
Ho: diff = 0                             Welch's degrees of freedom =  890.996

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3284         Pr(|T| > |t|) = 0.6567          Pr(T > t) = 0.6716

. ttest bestblacktot, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445     .458427    .0270417    .5704449    .4052814    .5115726
       1 |     446    .4820628    .0277974    .5870446    .4274324    .5366932
---------+--------------------------------------------------------------------
combined |     891    .4702581    .0193841    .5786092    .4322142    .5083021
---------+--------------------------------------------------------------------
    diff |           -.0236358    .0387807               -.0997481    .0524765
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.6095
Ho: diff = 0                             Welch's degrees of freedom =  890.376

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2712         Pr(|T| > |t|) = 0.5424          Pr(T > t) = 0.7288

. ttest bestblack1, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .0494382     .010288    .2170251     .029219    .0696574
       1 |     446     .073991    .0124084    .2620502    .0496046    .0983774
---------+--------------------------------------------------------------------
combined |     891    .0617284     .008067    .2407968    .0458958    .0775609
---------+--------------------------------------------------------------------
    diff |           -.0245528    .0161187               -.0561893    .0070836
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.5233
Ho: diff = 0                             Welch's degrees of freedom =   861.72

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0640         Pr(|T| > |t|) = 0.1281          Pr(T > t) = 0.9360

. 
. ttest bestwomanbin, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .7573034    .0203458    .4291957    .7173173    .7972895
       1 |     446    .7802691    .0196285    .4145293    .7416929    .8188452
---------+--------------------------------------------------------------------
combined |     891    .7687991    .0141321    .4218374     .741063    .7965352
---------+--------------------------------------------------------------------
    diff |           -.0229657    .0282707               -.0784507    .0325193
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.8123
Ho: diff = 0                             Welch's degrees of freedom =  889.779

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2084         Pr(|T| > |t|) = 0.4168          Pr(T > t) = 0.7916

. ttest bestwomantot, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .8741573    .0283637    .5983325    .8184136    .9299011
       1 |     446    .9304933    .0289941    .6123179    .8735109    .9874756
---------+--------------------------------------------------------------------
combined |     891    .9023569    .0202914    .6056894    .8625324    .9421814
---------+--------------------------------------------------------------------
    diff |            -.056336    .0405605               -.1359413    .0232694
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.3889
Ho: diff = 0                             Welch's degrees of freedom =  890.612

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0826         Pr(|T| > |t|) = 0.1652          Pr(T > t) = 0.9174

. ttest bestwoman1, by(shamingc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .1280899    .0158599     .334566      .09692    .1592598
       1 |     446    .1659193    .0176349    .3724259    .1312613    .2005773
---------+--------------------------------------------------------------------
combined |     891    .1470258    .0118705    .3543305    .1237283    .1703233
---------+--------------------------------------------------------------------
    diff |           -.0378294    .0237177               -.0843791    .0087203
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.5950
Ho: diff = 0                             Welch's degrees of freedom =  881.342

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0555         Pr(|T| > |t|) = 0.1111          Pr(T > t) = 0.9445

. 
. //For Table 3.4//
. **Role Model Treatment**
. 
. ttest bestblackbin, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .4202247     .023425    .4941504    .3741871    .4662623
       1 |     438    .5296804    .0238761     .499689    .4827542    .5766066
---------+--------------------------------------------------------------------
combined |     883    .4745187     .016814    .4996333    .4415186    .5075188
---------+--------------------------------------------------------------------
    diff |           -.1094556    .0334484               -.1751034   -.0438079
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.2724
Ho: diff = 0                             Welch's degrees of freedom =  882.356

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0006         Pr(|T| > |t|) = 0.0011          Pr(T > t) = 0.9994

. ttest bestblacktot, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445     .458427    .0270417    .5704449    .4052814    .5115726
       1 |     438    .5821918    .0282303    .5908166    .5267077    .6376758
---------+--------------------------------------------------------------------
combined |     883    .5198188    .0196398    .5836036    .4812726     .558365
---------+--------------------------------------------------------------------
    diff |           -.1237648    .0390922               -.2004896     -.04704
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.1660
Ho: diff = 0                             Welch's degrees of freedom =  880.713

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0008         Pr(|T| > |t|) = 0.0016          Pr(T > t) = 0.9992

. ttest bestblack1, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .0494382     .010288    .2170251     .029219    .0696574
       1 |     438    .1073059    .0148055    .3098557    .0782071    .1364047
---------+--------------------------------------------------------------------
combined |     883    .0781427    .0090374    .2685481    .0604054    .0958799
---------+--------------------------------------------------------------------
    diff |           -.0578677     .018029               -.0932586   -.0224769
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.2097
Ho: diff = 0                             Welch's degrees of freedom =  783.121

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0007         Pr(|T| > |t|) = 0.0014          Pr(T > t) = 0.9993

. 
. ttest bestwomanbin, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .7573034    .0203458    .4291957    .7173173    .7972895
       1 |     438    .8059361    .0189183    .3959306    .7687539    .8431182
---------+--------------------------------------------------------------------
combined |     883     .781427    .0139158    .4135124     .754115    .8087389
---------+--------------------------------------------------------------------
    diff |           -.0486327    .0277823                 -.10316    .0058946
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.7505
Ho: diff = 0                             Welch's degrees of freedom =   879.31

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0402         Pr(|T| > |t|) = 0.0804          Pr(T > t) = 0.9598

. ttest bestwomantot, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .8741573    .0283637    .5983325    .8184136    .9299011
       1 |     438    1.031963    .0316349    .6620689    .9697881    1.094139
---------+--------------------------------------------------------------------
combined |     883    .9524349    .0213802    .6353194    .9104729    .9943969
---------+--------------------------------------------------------------------
    diff |           -.1578062    .0424884               -.2411978   -.0744146
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.7141
Ho: diff = 0                             Welch's degrees of freedom =  871.123

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0001         Pr(|T| > |t|) = 0.0002          Pr(T > t) = 0.9999

. ttest bestwoman1, by(underc) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .1280899    .0158599     .334566      .09692    .1592598
       1 |     438     .216895    .0197149    .4126018    .1781472    .2556427
---------+--------------------------------------------------------------------
combined |     883    .1721404    .0127112    .3777164    .1471928    .1970881
---------+--------------------------------------------------------------------
    diff |           -.0888051    .0253025               -.1384684   -.0391417
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.5097
Ho: diff = 0                             Welch's degrees of freedom =  841.388

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0002         Pr(|T| > |t|) = 0.0005          Pr(T > t) = 0.9998

. 
. //For Table 3.5//
. **Black Voters Treatment**
. 
. ttest bestblacktot, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445     .458427    .0270417    .5704449    .4052814    .5115726
       1 |     447     .689038    .0321275    .6792509     .625898    .7521781
---------+--------------------------------------------------------------------
combined |     892     .573991    .0213453    .6375076     .532098     .615884
---------+--------------------------------------------------------------------
    diff |           -.2306111    .0419932               -.3130312   -.1481909
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.4916
Ho: diff = 0                             Welch's degrees of freedom =  867.344

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest bestblackbin, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .4202247     .023425    .4941504    .3741871    .4662623
       1 |     447    .5659955    .0234685    .4961808    .5198729    .6121182
---------+--------------------------------------------------------------------
combined |     892    .4932735    .0167491    .5002352    .4604012    .5261459
---------+--------------------------------------------------------------------
    diff |           -.1457708    .0331587                -.210849   -.0806926
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.3962
Ho: diff = 0                             Welch's degrees of freedom =      892

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest bestblack1, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .0494382     .010288    .2170251     .029219    .0696574
       1 |     447    .1588367    .0173081    .3659333    .1248212    .1928522
---------+--------------------------------------------------------------------
combined |     892    .1042601    .0102379    .3057691    .0841669    .1243533
---------+--------------------------------------------------------------------
    diff |           -.1093985    .0201348               -.1489278   -.0698692
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.4333
Ho: diff = 0                             Welch's degrees of freedom =  727.076

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. ttest bestwomantot, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .8741573    .0283637    .5983325    .8184136    .9299011
       1 |     447    .9463087    .0290905     .615042    .8891373     1.00348
---------+--------------------------------------------------------------------
combined |     892    .9103139    .0203405    .6074963    .8703931    .9502347
---------+--------------------------------------------------------------------
    diff |           -.0721514    .0406295                -.151892    .0075892
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.7758
Ho: diff = 0                             Welch's degrees of freedom =  891.525

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0381         Pr(|T| > |t|) = 0.0761          Pr(T > t) = 0.9619

. ttest bestwomanbin, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .7573034    .0203458    .4291957    .7173173    .7972895
       1 |     447    .7852349    .0194453    .4111194    .7470191    .8234507
---------+--------------------------------------------------------------------
combined |     892    .7713004    .0140704    .4202309    .7436855    .7989154
---------+--------------------------------------------------------------------
    diff |           -.0279315    .0281438               -.0831674    .0273044
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.9925
Ho: diff = 0                             Welch's degrees of freedom =  889.989

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1606         Pr(|T| > |t|) = 0.3212          Pr(T > t) = 0.8394

. ttest bestwoman1, by(blackcomp) welch

Two-sample t test with unequal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     445    .1280899    .0158599     .334566      .09692    .1592598
       1 |     447    .1856823    .0184126    .3892858    .1494961    .2218685
---------+--------------------------------------------------------------------
combined |     892    .1569507    .0121862    .3639583    .1330336    .1808677
---------+--------------------------------------------------------------------
    diff |           -.0575924    .0243015               -.1052885   -.0098963
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.3699
Ho: diff = 0                             Welch's degrees of freedom =  873.366

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0090         Pr(|T| > |t|) = 0.0180          Pr(T > t) = 0.9910

. 
. *****************************************************************************
> ***
. *********ANALYSIS 2**********************************************************
> ***
. *****************************************************************************
> ***
. 
. //APPENDIX TABLE 1.35//
. **Subject Demographics**
. 
. tab agegr

   agegroup |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        290       13.07       13.07
          2 |        951       42.86       55.93
          3 |        543       24.47       80.40
          4 |        240       10.82       91.21
          5 |        141        6.35       97.57
          6 |         48        2.16       99.73
          7 |          6        0.27      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab female

     female |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        953       42.95       42.95
          1 |      1,266       57.05      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab male

       male |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,288       58.04       58.04
          1 |        931       41.96      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab other

      other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,050       92.38       92.38
          1 |        169        7.62      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab white

      white |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        697       31.41       31.41
          1 |      1,522       68.59      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab black

      black |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,973       88.91       88.91
          1 |        246       11.09      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab hispanic

   hispanic |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,081       93.78       93.78
          1 |        138        6.22      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. tab api

        api |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,075       93.51       93.51
          1 |        144        6.49      100.00
------------+-----------------------------------
      Total |      2,219      100.00

. 
. //The manuscript also includes some discussion of candidate-specific results 
> from Study 3.//
. //That disucssion is based on the candidate-specific results below.//
. 
. reg warrentop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      4.04
       Model |  1.00852128         1  1.00852128   Prob > F        =    0.0446
    Residual |  221.981389       890  .249417291   R-squared       =    0.0045
-------------+----------------------------------   Adj R-squared   =    0.0034
       Total |   222.98991       891   .25026926   Root MSE        =    .49942

------------------------------------------------------------------------------
  warrentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0672498   .0334435    -2.01   0.045    -.1328872   -.0016125
       _cons |   .5303371   .0236746    22.40   0.000     .4838725    .5768017
------------------------------------------------------------------------------

. estimates store warren1

. reg harristop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =     20.15
       Model |  4.49211538         1  4.49211538   Prob > F        =    0.0000
    Residual |   198.37784       890  .222896449   R-squared       =    0.0221
-------------+----------------------------------   Adj R-squared   =    0.0210
       Total |  202.869955       891  .227687941   Root MSE        =    .47212

------------------------------------------------------------------------------
  harristop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |     .14193   .0316155     4.49   0.000     .0798803    .2039796
       _cons |   .2786517   .0223806    12.45   0.000     .2347268    .3225766
------------------------------------------------------------------------------

. estimates store harris1

. reg klobuchartop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.02
       Model |  .001425948         1  .001425948   Prob > F        =    0.8775
    Residual |  53.3561974       890  .059950784   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  53.3576233       891  .059885099   Root MSE        =    .24485

------------------------------------------------------------------------------
klobuchart~3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0025287   .0163963    -0.15   0.877    -.0347087    .0296512
       _cons |   .0651685   .0116069     5.61   0.000     .0423884    .0879487
------------------------------------------------------------------------------

. estimates store klobuchar1

. reg bidentop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      8.98
       Model |  1.49943895         1  1.49943895   Prob > F        =    0.0028
    Residual |  148.602579       890   .16696919   R-squared       =    0.0100
-------------+----------------------------------   Adj R-squared   =    0.0089
       Total |  150.102018       891  .168464667   Root MSE        =    .40862

------------------------------------------------------------------------------
   bidentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0819998   .0273632    -3.00   0.003    -.1357038   -.0282959
       _cons |   .8269663   .0193704    42.69   0.000     .7889493    .8649832
------------------------------------------------------------------------------

. estimates store biden1

. reg sanderstop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      2.36
       Model |  .511647797         1  .511647797   Prob > F        =    0.1249
    Residual |  193.066828       890   .21692902   R-squared       =    0.0026
-------------+----------------------------------   Adj R-squared   =    0.0015
       Total |  193.578475       891  .217259793   Root MSE        =    .46576

------------------------------------------------------------------------------
 sanderstop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0478999   .0311894    -1.54   0.125    -.1091133    .0133136
       _cons |    .705618    .022079    31.96   0.000     .6622851    .7489509
------------------------------------------------------------------------------

. estimates store sanders1

. reg buttigiegtop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.91
       Model |  .145615575         1  .145615575   Prob > F        =    0.3392
    Residual |  141.732187       890  .159249648   R-squared       =    0.0010
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  141.877803       891  .159234346   Root MSE        =    .39906

------------------------------------------------------------------------------
buttigiegt~3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0255536   .0267232    -0.96   0.339    -.0780014    .0268941
       _cons |    .211236   .0189173    11.17   0.000     .1741082    .2483637
------------------------------------------------------------------------------

. estimates store buttigieg1

. reg orourketop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.04
       Model |  .006452606         1  .006452606   Prob > F        =    0.8409
    Residual |  142.473368       890  .160082436   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  142.479821       891  .159910012   Root MSE        =     .4001

------------------------------------------------------------------------------
 orourketop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0053792   .0267929    -0.20   0.841    -.0579639    .0472055
       _cons |   .2022472   .0189667    10.66   0.000     .1650225    .2394719
------------------------------------------------------------------------------

. estimates store orourke1

. reg bookertop3 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =     10.17
       Model |  1.75373825         1  1.75373825   Prob > F        =    0.0015
    Residual |  153.403212       890   .17236316   R-squared       =    0.0113
-------------+----------------------------------   Adj R-squared   =    0.0102
       Total |  155.156951       891  .174137992   Root MSE        =    .41517

------------------------------------------------------------------------------
  bookertop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |   .0886811   .0278017     3.19   0.001     .0341166    .1432456
       _cons |   .1797753   .0196808     9.13   0.000     .1411491    .2184014
------------------------------------------------------------------------------

. estimates store booker1

. coefplot warren1 harris1 klobuchar1 biden1 sanders1 buttigieg1 orourke1 booke
> r1, drop(_cons) xline(0)

. 
. reg warrentop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.18
       Model |  .045488393         1  .045488393   Prob > F        =    0.6698
    Residual |  220.228577       881  .249975684   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  220.274066       882  .249743839   Root MSE        =    .49998

------------------------------------------------------------------------------
  warrentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0143553   .0336521    -0.43   0.670     -.080403    .0516923
       _cons |   .5303371   .0237011    22.38   0.000     .4838198    .5768543
------------------------------------------------------------------------------

. estimates store warren2

. reg harristop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =     14.08
       Model |  3.10531523         1  3.10531523   Prob > F        =    0.0002
    Residual |  194.323903       881  .220571967   R-squared       =    0.0157
-------------+----------------------------------   Adj R-squared   =    0.0146
       Total |  197.429219       882  .223842651   Root MSE        =    .46965

------------------------------------------------------------------------------
  harristop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .1186086    .031611     3.75   0.000     .0565669    .1806503
       _cons |   .2786517   .0222636    12.52   0.000     .2349558    .3223475
------------------------------------------------------------------------------

. estimates store harris2

. reg klobuchartop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      7.65
       Model |  .633052546         1  .633052546   Prob > F        =    0.0058
    Residual |  72.9365964       881  .082788418   R-squared       =    0.0086
-------------+----------------------------------   Adj R-squared   =    0.0075
       Total |  73.5696489       882    .0834123   Root MSE        =    .28773

------------------------------------------------------------------------------
klobuchart~3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0535529   .0193664     2.77   0.006     .0155433    .0915625
       _cons |   .0651685   .0136397     4.78   0.000     .0383984    .0919386
------------------------------------------------------------------------------

. estimates store klobuchar2

. reg bidentop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      5.27
       Model |   .85196496         1   .85196496   Prob > F        =    0.0219
    Residual |  142.454943       881  .161696871   R-squared       =    0.0059
-------------+----------------------------------   Adj R-squared   =    0.0048
       Total |  143.306908       882  .162479488   Root MSE        =    .40212

------------------------------------------------------------------------------
   bidentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0621261   .0270654    -2.30   0.022    -.1152463    -.009006
       _cons |   .8269663   .0190621    43.38   0.000     .7895539    .8643787
------------------------------------------------------------------------------

. estimates store biden2

. reg sanderstop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =     10.43
       Model |  2.33625674         1  2.33625674   Prob > F        =    0.0013
    Residual |  197.312667       881  .223964435   R-squared       =    0.0117
-------------+----------------------------------   Adj R-squared   =    0.0106
       Total |  199.648924       882  .226359324   Root MSE        =    .47325

------------------------------------------------------------------------------
 sanderstop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.1028783   .0318532    -3.23   0.001    -.1653952   -.0403613
       _cons |    .705618   .0224341    31.45   0.000     .6615874    .7496486
------------------------------------------------------------------------------

. estimates store sanders2

. reg buttigiegtop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      1.33
       Model |  .210360986         1  .210360986   Prob > F        =    0.2484
    Residual |  138.894962       881  .157656029   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0004
       Total |  139.105323       882  .157715785   Root MSE        =    .39706

------------------------------------------------------------------------------
buttigiegt~3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0308707   .0267251    -1.16   0.248    -.0833229    .0215815
       _cons |    .211236   .0188224    11.22   0.000      .174294     .248178
------------------------------------------------------------------------------

. estimates store buttigieg2

. reg orourketop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      1.40
       Model |  .239110417         1  .239110417   Prob > F        =    0.2372
    Residual |  150.576292       881    .1709152   R-squared       =    0.0016
-------------+----------------------------------   Adj R-squared   =    0.0005
       Total |  150.815402       882  .170992519   Root MSE        =    .41342

------------------------------------------------------------------------------
 orourketop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0329126   .0278262     1.18   0.237    -.0217007     .087526
       _cons |   .2022472   .0195979    10.32   0.000     .1637831    .2407113
------------------------------------------------------------------------------

. estimates store orourke2

. reg bookertop3 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.04
       Model |  .005868638         1  .005868638   Prob > F        =    0.8429
    Residual |  131.638525       881  .149419439   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  131.644394       882  .149256683   Root MSE        =    .38655

------------------------------------------------------------------------------
  bookertop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0051562   .0260176     0.20   0.843    -.0459074    .0562199
       _cons |   .1797753   .0183241     9.81   0.000     .1438112    .2157393
------------------------------------------------------------------------------

. estimates store booker2

. coefplot warren2 harris2 klobuchar2 biden2 sanders2 buttigieg2 orourke2 booke
> r2, drop(_cons) xline(0)

. 
. reg warrentop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      1.30
       Model |  .324590319         1  .324590319   Prob > F        =    0.2549
    Residual |  221.562797       886  .250070877   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |  221.887387       887   .25015489   Root MSE        =    .50007

-------------------------------------------------------------------------------
-
    warrentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0382378   .0335626    -1.14   0.255    -.1041093    .027633
> 8
         _cons |   .5303371   .0237056    22.37   0.000     .4838113    .576862
> 8
-------------------------------------------------------------------------------
-

. estimates store warren3

. reg harristop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      3.48
       Model |  .738879088         1  .738879088   Prob > F        =    0.0626
    Residual |  188.332067       886   .21256441   R-squared       =    0.0039
-------------+----------------------------------   Adj R-squared   =    0.0028
       Total |  189.070946       887  .213157774   Root MSE        =    .46105

-------------------------------------------------------------------------------
-
    harristop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0576914   .0309435     1.86   0.063    -.0030397    .118422
> 6
         _cons |   .2786517   .0218557    12.75   0.000     .2357566    .321546
> 7
-------------------------------------------------------------------------------
-

. estimates store harris3

. reg klobuchartop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.17
       Model |   .01108474         1   .01108474   Prob > F        =    0.6776
    Residual |  56.7985999       886  .064106772   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  56.8096847       887  .064046995   Root MSE        =    .25319

-------------------------------------------------------------------------------
-
 klobuchartop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0070662   .0169933     0.42   0.678    -.0262855     .04041
> 8
         _cons |   .0651685   .0120025     5.43   0.000     .0416119    .088725
> 2
-------------------------------------------------------------------------------
-

. estimates store klobuchar3

. reg bidentop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      2.98
       Model |  .468278563         1  .468278563   Prob > F        =    0.0849
    Residual |  139.437127       886  .157378247   R-squared       =    0.0033
-------------+----------------------------------   Adj R-squared   =    0.0022
       Total |  139.905405       887  .157728755   Root MSE        =    .39671

-------------------------------------------------------------------------------
-
     bidentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0459279   .0266255    -1.72   0.085    -.0981842    .006328
> 4
         _cons |   .8269663   .0188058    43.97   0.000     .7900571    .863875
> 4
-------------------------------------------------------------------------------
-

. estimates store biden3

. reg sanderstop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.23
       Model |  .049107885         1  .049107885   Prob > F        =    0.6297
    Residual |  187.068009       886  .211137708   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  187.117117       887  .210955036   Root MSE        =     .4595

-------------------------------------------------------------------------------
-
   sanderstop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0148731   .0308395    -0.48   0.630    -.0754001     .04565
> 4
         _cons |    .705618   .0217823    32.39   0.000     .6628671    .748368
> 8
-------------------------------------------------------------------------------
-

. estimates store sanders3

. reg buttigiegtop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.47
       Model |  .080245828         1  .080245828   Prob > F        =    0.4951
    Residual |  152.658493       886  .172300782   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0006
       Total |  152.738739       887     .172197   Root MSE        =    .41509

-------------------------------------------------------------------------------
-
 buttigiegtop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0190124   .0278592     0.68   0.495    -.0356653      .0736
> 9
         _cons |    .211236   .0196772    10.74   0.000     .1726166    .249855
> 3
-------------------------------------------------------------------------------
-

. estimates store buttigieg3

. reg orourketop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.60
       Model |  .100049339         1  .100049339   Prob > F        =    0.4402
    Residual |  148.673599       886  .167803159   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  148.773649       887  .167726774   Root MSE        =    .40964

-------------------------------------------------------------------------------
-
   orourketop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0212291   .0274932     0.77   0.440    -.0327302    .075188
> 4
         _cons |   .2022472   .0194187    10.42   0.000     .1641352    .240359
> 2
-------------------------------------------------------------------------------
-

. estimates store orourke3

. reg bookertop3 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.05
       Model |  .007886767         1  .007886767   Prob > F        =    0.8162
    Residual |   129.23423       886  .145862562   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  129.242117       887  .145707009   Root MSE        =    .38192

-------------------------------------------------------------------------------
-
    bookertop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0059604   .0256328    -0.23   0.816    -.0562685    .044347
> 8
         _cons |   .1797753   .0181047     9.93   0.000     .1442421    .215308
> 4
-------------------------------------------------------------------------------
-

. estimates store booker3

. coefplot warren3 harris3 klobuchar3 biden3 sanders3 buttigieg3 orourke3 booke
> r3, drop(_cons) xline(0)

. 
. reg warrentop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.32
       Model |  .080330358         1  .080330358   Prob > F        =    0.5701
    Residual |  221.255248       889  .248881043   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0008
       Total |  221.335578       890  .248691661   Root MSE        =    .49888

-------------------------------------------------------------------------------
-
    warrentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0189903   .0334262     0.57   0.570    -.0466132    .084593
> 8
         _cons |   .5303371   .0236492    22.43   0.000     .4839224    .576751
> 8
-------------------------------------------------------------------------------
-

. estimates store warren4

. reg harristop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      1.87
       Model |  .392483763         1  .392483763   Prob > F        =    0.1718
    Residual |  186.597415       889  .209895855   R-squared       =    0.0021
-------------+----------------------------------   Adj R-squared   =    0.0010
       Total |  186.989899       890   .21010101   Root MSE        =    .45814

-------------------------------------------------------------------------------
-
    harristop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0419761   .0306968     1.37   0.172    -.0182706    .102222
> 8
         _cons |   .2786517   .0217181    12.83   0.000      .236027    .321276
> 4
-------------------------------------------------------------------------------
-

. estimates store harris4

. reg klobuchartop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.08
       Model |  .004775935         1  .004775935   Prob > F        =    0.7761
    Residual |  52.4755832       889  .059027653   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0010
       Total |  52.4803591       890  .058966696   Root MSE        =    .24296

-------------------------------------------------------------------------------
-
 klobuchartop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0046304   .0162787    -0.28   0.776    -.0365795    .027318
> 7
         _cons |   .0651685   .0115172     5.66   0.000     .0425644    .087772
> 7
-------------------------------------------------------------------------------
-

. estimates store klobuchar4

. reg bidentop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.58
       Model |  .087250994         1  .087250994   Prob > F        =    0.4454
    Residual |  133.093445       889  .149711412   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  133.180696       890  .149641231   Root MSE        =    .38693

-------------------------------------------------------------------------------
-
     bidentop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0197914    .025925    -0.76   0.445    -.0706728    .031089
> 9
         _cons |   .8269663    .018342    45.09   0.000     .7909676     .86296
> 5
-------------------------------------------------------------------------------
-

. estimates store biden4

. reg sanderstop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      3.92
       Model |  .859573724         1  .859573724   Prob > F        =    0.0479
    Residual |  194.752099       889  .219068727   R-squared       =    0.0044
-------------+----------------------------------   Adj R-squared   =    0.0033
       Total |  195.611672       890  .219788396   Root MSE        =    .46805

-------------------------------------------------------------------------------
-
   sanderstop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0621202   .0313604    -1.98   0.048    -.1236693   -.000571
> 2
         _cons |    .705618   .0221876    31.80   0.000     .6620718    .749164
> 1
-------------------------------------------------------------------------------
-

. estimates store sanders4

. reg buttigiegtop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.89
       Model |  .155626661         1  .155626661   Prob > F        =    0.3450
    Residual |  154.950995       889  .174298082   R-squared       =    0.0010
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  155.106622       890  .174277103   Root MSE        =    .41749

-------------------------------------------------------------------------------
-
 buttigiegtop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0264322   .0279729     0.94   0.345    -.0284684    .081332
> 9
         _cons |    .211236   .0197909    10.67   0.000     .1723936    .250078
> 4
-------------------------------------------------------------------------------
-

. estimates store buttigieg4

. reg orourketop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.41
       Model |  .068090477         1  .068090477   Prob > F        =    0.5230
    Residual |  148.264121       889  .166776289   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0007
       Total |  148.332211       890  .166665406   Root MSE        =    .40838

-------------------------------------------------------------------------------
-
   orourketop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0174838   .0273627     0.64   0.523    -.0362192    .071186
> 7
         _cons |   .2022472   .0193592    10.45   0.000     .1642522    .240242
> 2
-------------------------------------------------------------------------------
-

. estimates store orourke4

. reg bookertop3 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.53
       Model |  .074925594         1  .074925594   Prob > F        =    0.4674
    Residual |  125.994659       889  .141726276   R-squared       =    0.0006
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  126.069585       890  .141651219   Root MSE        =    .37647

-------------------------------------------------------------------------------
-
    bookertop3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0183403   .0252242    -0.73   0.467    -.0678461    .031165
> 5
         _cons |   .1797753   .0178462    10.07   0.000     .1447497    .214800
> 8
-------------------------------------------------------------------------------
-

. estimates store booker4

. coefplot warren4 harris4 klobuchar4 biden4 sanders4 buttigieg4 orourke4 booke
> r4, drop(_cons) xline(0)

. 
. 
. reg warren1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.34
       Model |  .030256569         1  .030256569   Prob > F        =    0.5582
    Residual |  78.4843174       890  .088184626   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0007
       Total |   78.514574       891  .088119612   Root MSE        =    .29696

------------------------------------------------------------------------------
     warren1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0116482   .0198859    -0.59   0.558    -.0506769    .0273805
       _cons |   .1033708   .0140772     7.34   0.000     .0757424    .1309992
------------------------------------------------------------------------------

. estimates store warren7

. reg harris1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =     17.22
       Model |  .811171959         1  .811171959   Prob > F        =    0.0000
    Residual |  41.9186487       890  .047099605   R-squared       =    0.0190
-------------+----------------------------------   Adj R-squared   =    0.0179
       Total |  42.7298206       891   .04795715   Root MSE        =    .21702

------------------------------------------------------------------------------
     harris1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |   .0603122   .0145331     4.15   0.000     .0317891    .0888353
       _cons |   .0202247   .0102879     1.97   0.050     .0000333    .0404162
------------------------------------------------------------------------------

. estimates store harris7

. reg klobuchar1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      2.00
       Model |  .017776798         1  .017776798   Prob > F        =    0.1576
    Residual |  7.91047432       890  .008888173   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =    0.0011
       Total |  7.92825112       891  .008898149   Root MSE        =    .09428

------------------------------------------------------------------------------
  klobuchar1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |   .0089284   .0063133     1.41   0.158    -.0034622    .0213191
       _cons |   .0044944   .0044692     1.01   0.315    -.0042769    .0132657
------------------------------------------------------------------------------

. estimates store klobuchar7

. reg biden1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      2.37
       Model |  .591665593         1  .591665593   Prob > F        =    0.1241
    Residual |  222.246899       890  .249715617   R-squared       =    0.0027
-------------+----------------------------------   Adj R-squared   =    0.0015
       Total |  222.838565       891    .2500994   Root MSE        =    .49972

------------------------------------------------------------------------------
      biden1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0515094   .0334635    -1.54   0.124     -.117186    .0141672
       _cons |   .5123596   .0236888    21.63   0.000     .4658672    .5588519
------------------------------------------------------------------------------

. estimates store biden7

. reg sanders1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      3.43
       Model |  .617700583         1  .617700583   Prob > F        =    0.0645
    Residual |  160.470864       890  .180304342   R-squared       =    0.0038
-------------+----------------------------------   Adj R-squared   =    0.0027
       Total |  161.088565       891  .180795247   Root MSE        =    .42462

------------------------------------------------------------------------------
    sanders1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0526305   .0284349    -1.85   0.065    -.1084378    .0031768
       _cons |   .2629213    .020129    13.06   0.000     .2234154    .3024273
------------------------------------------------------------------------------

. estimates store sanders7

. reg buttigieg1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.98
       Model |  .028709837         1  .028709837   Prob > F        =    0.3232
    Residual |  26.1540256       890  .029386546   R-squared       =    0.0011
-------------+----------------------------------   Adj R-squared   =   -0.0000
       Total |  26.1827354       891  .029385786   Root MSE        =    .17143

------------------------------------------------------------------------------
  buttigieg1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |  -.0113466   .0114795    -0.99   0.323    -.0338766    .0111835
       _cons |   .0359551   .0081263     4.42   0.000     .0200061    .0519041
------------------------------------------------------------------------------

. estimates store buttigieg7

. reg orourke1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =      0.50
       Model |   .01729959         1   .01729959   Prob > F        =    0.4800
    Residual |  30.8347183       890  .034645751   R-squared       =    0.0006
-------------+----------------------------------   Adj R-squared   =   -0.0006
       Total |  30.8520179       891  .034626283   Root MSE        =    .18613

------------------------------------------------------------------------------
    orourke1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |   .0088078   .0124645     0.71   0.480    -.0156554     .033271
       _cons |   .0314607   .0088236     3.57   0.000     .0141432    .0487781
------------------------------------------------------------------------------

. estimates store orourke7

. reg booker1 blackc

      Source |       SS           df       MS      Number of obs   =       892
-------------+----------------------------------   F(1, 890)       =     10.66
       Model |   .53730781         1   .53730781   Prob > F        =    0.0011
    Residual |  44.8797325       890  .050426666   R-squared       =    0.0118
-------------+----------------------------------   Adj R-squared   =    0.0107
       Total |  45.4170404       891  .050973109   Root MSE        =    .22456

------------------------------------------------------------------------------
     booker1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
blackcompare |   .0490863   .0150376     3.26   0.001      .019573    .0785996
       _cons |   .0292135   .0106451     2.74   0.006      .008321    .0501059
------------------------------------------------------------------------------

. estimates store booker7

. coefplot warren7 harris7 klobuchar7 biden7 sanders7 buttigieg7 orourke7 booke
> r7, drop(_cons) xline(0)

. 
. reg warren1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.17
       Model |  .015953287         1  .015953287   Prob > F        =    0.6840
    Residual |  84.7632087       881  .096212496   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  84.7791619       882  .096121499   Root MSE        =    .31018

------------------------------------------------------------------------------
     warren1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0085014   .0208775     0.41   0.684    -.0324741    .0494769
       _cons |   .1033708    .014704     7.03   0.000     .0745118    .1322298
------------------------------------------------------------------------------

. estimates store warren8

. reg harris1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =     15.95
       Model |  .727294143         1  .727294143   Prob > F        =    0.0001
    Residual |  40.1787081       881  .045605798   R-squared       =    0.0178
-------------+----------------------------------   Adj R-squared   =    0.0167
       Total |  40.9060023       882  .046378687   Root MSE        =    .21356

------------------------------------------------------------------------------
     harris1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0574009   .0143739     3.99   0.000     .0291899    .0856119
       _cons |   .0202247   .0101235     2.00   0.046     .0003558    .0400937
------------------------------------------------------------------------------

. estimates store harris8

. reg klobuchar1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      7.47
       Model |  .115785332         1  .115785332   Prob > F        =    0.0064
    Residual |  13.6622441       881  .015507655   R-squared       =    0.0084
-------------+----------------------------------   Adj R-squared   =    0.0073
       Total |  13.7780294       882  .015621349   Root MSE        =    .12453

------------------------------------------------------------------------------
  klobuchar1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0229029   .0083818     2.73   0.006     .0064523    .0393535
       _cons |   .0044944   .0059033     0.76   0.447    -.0070917    .0160805
------------------------------------------------------------------------------

. estimates store klobuchar8

. reg biden1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      5.49
       Model |  1.36264416         1  1.36264416   Prob > F        =    0.0194
    Residual |  218.761931       881  .248310932   R-squared       =    0.0062
-------------+----------------------------------   Adj R-squared   =    0.0051
       Total |  220.124575       882  .249574348   Root MSE        =    .49831

------------------------------------------------------------------------------
      biden1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0785696   .0335399    -2.34   0.019    -.1443969   -.0127422
       _cons |   .5123596   .0236221    21.69   0.000     .4659975    .5587216
------------------------------------------------------------------------------

. estimates store biden8

. reg sanders1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.76
       Model |  .143290898         1  .143290898   Prob > F        =    0.3828
    Residual |  165.544138       881  .187904811   R-squared       =    0.0009
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  165.687429       882  .187854228   Root MSE        =    .43348

------------------------------------------------------------------------------
    sanders1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0254784   .0291764    -0.87   0.383    -.0827419     .031785
       _cons |   .2629213   .0205489    12.79   0.000     .2225908    .3032519
------------------------------------------------------------------------------

. estimates store sanders8

. reg buttigieg1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.11
       Model |  .003516935         1  .003516935   Prob > F        =    0.7437
    Residual |  28.9772305       881  .032891295   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0010
       Total |  28.9807475       882   .03285799   Root MSE        =    .18136

------------------------------------------------------------------------------
  buttigieg1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |  -.0039916   .0122069    -0.33   0.744    -.0279495    .0199663
       _cons |   .0359551   .0085973     4.18   0.000     .0190815    .0528286
------------------------------------------------------------------------------

. estimates store buttigieg8

. reg orourke1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      1.99
       Model |    .0777486         1    .0777486   Prob > F        =    0.1589
    Residual |  34.4545277       881  .039108431   R-squared       =    0.0023
-------------+----------------------------------   Adj R-squared   =    0.0011
       Total |  34.5322763       882  .039152241   Root MSE        =    .19776

------------------------------------------------------------------------------
    orourke1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0187676   .0133106     1.41   0.159    -.0073566    .0448919
       _cons |   .0314607   .0093747     3.36   0.001     .0130614    .0498599
------------------------------------------------------------------------------

. estimates store orourke8

. reg booker1 underc

      Source |       SS           df       MS      Number of obs   =       883
-------------+----------------------------------   F(1, 881)       =      0.00
       Model |  .000048116         1  .000048116   Prob > F        =    0.9673
    Residual |    25.23438       881  .028642883   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  25.2344281       882  .028610463   Root MSE        =    .16924

------------------------------------------------------------------------------
     booker1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
undercompare |   .0004669   .0113913     0.04   0.967    -.0218903    .0228241
       _cons |   .0292135   .0080228     3.64   0.000     .0134674    .0449596
------------------------------------------------------------------------------

. estimates store booker8

. coefplot warren8 harris8 klobuchar8 biden8 sanders8 buttigieg8 orourke8 booke
> r8, drop(_cons) xline(0)

. 
. reg warren1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.01
       Model |  .000711824         1  .000711824   Prob > F        =    0.9300
    Residual |  81.6738377       886  .092182661   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  81.6745495       887  .092079537   Root MSE        =    .30362

-------------------------------------------------------------------------------
-
       warren1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0017907   .0203774    -0.09   0.930    -.0417843     .03820
> 3
         _cons |   .1033708   .0143928     7.18   0.000     .0751229    .131618
> 7
-------------------------------------------------------------------------------
-

. estimates store warren9

. reg harris1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      7.19
       Model |  .255896854         1  .255896854   Prob > F        =    0.0075
    Residual |  31.5177518       886  .035573083   R-squared       =    0.0081
-------------+----------------------------------   Adj R-squared   =    0.0069
       Total |  31.7736486       887  .035821475   Root MSE        =    .18861

-------------------------------------------------------------------------------
-
       harris1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0339514   .0126586     2.68   0.007      .009107    .058795
> 7
         _cons |   .0202247   .0089409     2.26   0.024     .0026769    .037772
> 5
-------------------------------------------------------------------------------
-

. estimates store harris9

. reg klobuchar1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.68
       Model |  .004565605         1  .004565605   Prob > F        =    0.4101
    Residual |  5.95489385       886  .006721099   R-squared       =    0.0008
-------------+----------------------------------   Adj R-squared   =   -0.0004
       Total |  5.95945946       887  .006718669   Root MSE        =    .08198

-------------------------------------------------------------------------------
-
    klobuchar1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |    .004535   .0055023     0.82   0.410    -.0062641     .01533
> 4
         _cons |   .0044944   .0038863     1.16   0.248    -.0031331    .012121
> 9
-------------------------------------------------------------------------------
-

. estimates store klobuchar9

. reg biden1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.00
       Model |  .001187826         1  .001187826   Prob > F        =    0.9451
    Residual |   221.83665       886  .250379966   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  221.837838       887  .250099028   Root MSE        =    .50038

-------------------------------------------------------------------------------
-
        biden1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0023131   .0335834     0.07   0.945    -.0635991    .068225
> 4
         _cons |   .5123596   .0237203    21.60   0.000     .4658051     .55891
> 4
-------------------------------------------------------------------------------
-

. estimates store biden9

. reg sanders1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      1.10
       Model |  .205374512         1  .205374512   Prob > F        =    0.2944
    Residual |  165.290121       886  .186557699   R-squared       =    0.0012
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  165.495495       887  .186578913   Root MSE        =    .43192

-------------------------------------------------------------------------------
-
      sanders1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0304157   .0289889    -1.05   0.294    -.0873106    .026479
> 1
         _cons |   .2629213   .0204751    12.84   0.000     .2227359    .303106
> 8
-------------------------------------------------------------------------------
-

. estimates store sanders9

. reg buttigieg1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.31
       Model |  .009698669         1  .009698669   Prob > F        =    0.5800
    Residual |  28.0432293       886    .0316515   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0008
       Total |  28.0529279       887  .031626751   Root MSE        =    .17791

-------------------------------------------------------------------------------
-
    buttigieg1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0066097   .0119405    -0.55   0.580    -.0300446    .016825
> 2
         _cons |   .0359551   .0084337     4.26   0.000     .0194027    .052507
> 4
-------------------------------------------------------------------------------
-

. estimates store buttigieg9

. reg orourke1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.04
       Model |  .001278043         1  .001278043   Prob > F        =    0.8408
    Residual |  28.0516499       886  .031661004   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  28.0529279       887  .031626751   Root MSE        =    .17794

-------------------------------------------------------------------------------
-
      orourke1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |   .0023994   .0119423     0.20   0.841    -.0210391    .025837
> 8
         _cons |   .0314607   .0084349     3.73   0.000     .0149059    .048015
> 5
-------------------------------------------------------------------------------
-

. estimates store orourke9

. reg booker1 correctc

      Source |       SS           df       MS      Number of obs   =       888
-------------+----------------------------------   F(1, 886)       =      0.16
       Model |   .00426433         1   .00426433   Prob > F        =    0.6876
    Residual |   23.347087       886  .026351114   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  23.3513514       887  .026326213   Root MSE        =    .16233

-------------------------------------------------------------------------------
-
       booker1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
correctcompare |  -.0043828   .0108949    -0.40   0.688    -.0257656    .017000
> 1
         _cons |   .0292135   .0076952     3.80   0.000     .0141106    .044316
> 4
-------------------------------------------------------------------------------
-

. estimates store booker9

. coefplot warren9 harris9 klobuchar9 biden9 sanders9 buttigieg9 orourke9 booke
> r9, drop(_cons) xline(0)

. 
. reg warren1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.05
       Model |  .004954264         1  .004954264   Prob > F        =    0.8156
    Residual |  80.9041366       889  .091005778   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  80.9090909       890  .090909091   Root MSE        =    .30167

-------------------------------------------------------------------------------
-
       warren1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0047161   .0202128    -0.23   0.816    -.0443864    .034954
> 2
         _cons |   .1033708   .0143006     7.23   0.000     .0753039    .131437
> 7
-------------------------------------------------------------------------------
-

. estimates store warren11

. reg harris1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      7.84
       Model |  .285949133         1  .285949133   Prob > F        =    0.0052
    Residual |  32.4166322       889  .036464153   R-squared       =    0.0087
-------------+----------------------------------   Adj R-squared   =    0.0076
       Total |  32.7025814       890  .036744473   Root MSE        =    .19096

-------------------------------------------------------------------------------
-
       harris1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0358291   .0127945     2.80   0.005     .0107181    .060940
> 1
         _cons |   .0202247   .0090522     2.23   0.026     .0024586    .037990
> 8
-------------------------------------------------------------------------------
-

. estimates store harris11

. reg klobuchar1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      1.29
       Model |  .010048187         1  .010048187   Prob > F        =    0.2567
    Residual |  6.93495742       889  .007800852   R-squared       =    0.0014
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |  6.94500561       890  .007803377   Root MSE        =    .08832

-------------------------------------------------------------------------------
-
    klobuchar1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0067164   .0059178     1.13   0.257    -.0048982    .018330
> 9
         _cons |   .0044944   .0041869     1.07   0.283    -.0037229    .012711
> 7
-------------------------------------------------------------------------------
-

. estimates store klobuchar11

. reg biden1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.01
       Model |   .00247824         1   .00247824   Prob > F        =    0.9208
    Residual |  222.572157       889  .250362381   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  222.574635       890   .25008386   Root MSE        =    .50036

-------------------------------------------------------------------------------
-
        biden1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0033355   .0335256     0.10   0.921    -.0624629     .06913
> 4
         _cons |   .5123596   .0237194    21.60   0.000     .4658069    .558912
> 2
-------------------------------------------------------------------------------
-

. estimates store biden11

. reg sanders1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      1.06
       Model |   .19698168         1   .19698168   Prob > F        =    0.3046
    Residual |  165.987081       889  .186712127   R-squared       =    0.0012
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  166.184063       890  .186723666   Root MSE        =     .4321

-------------------------------------------------------------------------------
-
      sanders1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0297375   .0289519    -1.03   0.305    -.0865596    .027084
> 6
         _cons |   .2629213   .0204836    12.84   0.000     .2227195    .303123
> 2
-------------------------------------------------------------------------------
-

. estimates store sanders11

. reg buttigieg1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.03
       Model |  .001040739         1  .001040739   Prob > F        =    0.8645
    Residual |   31.776737       889  .035744361   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  31.7777778       890  .035705368   Root MSE        =    .18906

-------------------------------------------------------------------------------
-
    buttigieg1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |   .0021615   .0126676     0.17   0.865    -.0227004    .027023
> 5
         _cons |   .0359551   .0089624     4.01   0.000     .0183651     .05354
> 5
-------------------------------------------------------------------------------
-

. estimates store buttigieg11

. reg orourke1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      0.04
       Model |  .001191387         1  .001191387   Prob > F        =    0.8406
    Residual |  26.1806268       889  .029449524   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0011
       Total |  26.1818182       890  .029417773   Root MSE        =    .17161

-------------------------------------------------------------------------------
-
      orourke1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0023127   .0114982    -0.20   0.841    -.0248795    .020254
> 1
         _cons |   .0314607    .008135     3.87   0.000     .0154946    .047426
> 8
-------------------------------------------------------------------------------
-

. estimates store orourke11

. reg booker1 shamingc

      Source |       SS           df       MS      Number of obs   =       891
-------------+----------------------------------   F(1, 889)       =      1.23
       Model |  .028323544         1  .028323544   Prob > F        =    0.2678
    Residual |   20.476727       889  .023033439   R-squared       =    0.0014
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |  20.5050505       890  .023039383   Root MSE        =    .15177

-------------------------------------------------------------------------------
-
       booker1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval
> ]
---------------+---------------------------------------------------------------
-
shamingcompare |  -.0112763   .0101688    -1.11   0.268     -.031234    .008681
> 4
         _cons |   .0292135   .0071945     4.06   0.000     .0150933    .043333
> 6
-------------------------------------------------------------------------------
-

. estimates store booker11

. coefplot warren11 harris11 klobuchar11 biden11 sanders11 buttigieg11 orourke1
> 1 booker11, drop(_cons) xline(0)

. 
. clear 

. 
. //Congratulations! You have reached the end of the do-file for Study 3.//
. 
. //Any questions? Please consult the Read.Me file for this replication package
>  or email gina.bateson@gmail.com .//
. 
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/gina/Dropbox (Personal)/Strategic Discrimination resubmit/P
> erspectives Final Submission/Data and Replication Files/stratdisc.smcl
  log type:  smcl
 closed on:  22 Jun 2020, 12:38:03
-------------------------------------------------------------------------------
